**Meet the editor**

Dr. Petrică Vizureanu was born on the 17th of October 1967, in Barlad, Romania. He received the following degrees: M.Sc. in Heating Equipment, from "Gh. Asachi" Technical University of Iasi (1992), and Ph.D. at "Gh. Asachi" Technical University of Iasi (1999). Vizureanu's past and present positions include Assistant (1993-1999), Lecturer (1999-2002), Ass. Professor (2002-2009), Profes-

sor (2009-today) in "Gh. Asachi" Technical University of Iasi – Romania. His research activity is in Expert System for Heating System Programming, Computer Assisted Design for Heating Equipment, Heating Equipment for Materials Processing, Heat Transfer. Vizureanu's publications consist of 36 papers in international journals and conferences (proceedings) and 14 books.

Contents

**Preface IX** 

Chapter 1 **Expert System for Identification of Sport Talents: Idea, Implementation and Results 3** 

> **for the Formulation of Drugs in Solid Form 17**  Josep M. Suñé Negre, Encarna García Montoya, Pilar Pérez Lozano, Johnny E. Aguilar Díaz, Manel Roig Carreras, Roser Fuster García,

Montserrat Miñarro Carmona and Josep R. Ticó Grau

**on Fuzzy Expert System and Decision Trees 35** 

**of Result Based Career Selection Based** 

**for Personal Expert Systems 51** 

Vladan Papić, Nenad Rogulj and Vladimir Pleština

Chapter 2 **SeDeM Diagram: A New Expert System** 

Chapter 3 **Parametric Modeling and Prognosis** 

Chapter 5 **AI Applications in Psychology 75**  Zaharia Mihai Horia

Chapter 7 **Advances in Health Monitoring and Management 109**  Nezih Mrad and Rim Lejmi-Mrad

Chapter 6 **An Expert System to Support the Design of Human-Computer Interfaces 93**  Cecilia Sosa Arias Peixoto and Tiago Cinto

Avneet Dhawan

Chapter 4 **Question-Answer Shell** 

Petr Sosnin

**Part 1 Human 1** 

## Contents


**Part 1 Human 1** 


#### X Contents

## **Part 2 Materials Processing 137**  Chapter 8 **Expert System for Simulation of Metal Sheet Stamping: How Automation Can Help Improving Models and Manufacturing Techniques 139**  Alejandro Quesada, Antonio Gauchía, Carolina Álvarez-Caldas and José-Luis San- Román Chapter 9 **Expert System Used on Materials Processing 161**  Vizureanu Petrică

Chapter 10 **Interface Layers Detection in Oil Field Tanks: A Critical Review 181**  Mahmoud Meribout, Ahmed Al Naamany and Khamis Al Busaidi Contents VII

Chapter 18 **Fuzzy Based Flow Management of Real-Time** 

Tapio Frantti and Mikko Majanen

Chapter 19 **Expert System for Automatic Analysis** 

**Traffic for Quality of Service in WLANs 351**

Joze Mohorko, Sasa Klampfer, Matjaz Fras and Zarko Cucej

**of Results of Network Simulation 377** 

	- **Part 3 Automation & Control 237**

Chapter 18 **Fuzzy Based Flow Management of Real-Time Traffic for Quality of Service in WLANs 351**  Tapio Frantti and Mikko Majanen

VI Contents

**Part 2 Materials Processing 137** 

Vizureanu Petrică

Chapter 12 **Expert System Development**

and S. Ataolah Sadr

**Part 3 Automation & Control 237** 

and Héctor Alaiz-Moreton

Maria Meler-Kapcia

**A Critical Review 181**

Chapter 8 **Expert System for Simulation of Metal Sheet Stamping: How Automation Can Help Improving Models** 

Carolina Álvarez-Caldas and José-Luis San- Román

Mahmoud Meribout, Ahmed Al Naamany and Khamis Al Busaidi

Nassereldeen A. K, Mohammed Saedi and Nur Adibah Md Azman

**for Acoustic Analysis in Concrete Harbor NDT 221** 

Mohammad Reza Hedayati, Ali Asghar Amidian

Chapter 13 **Conceptual Model Development for a Knowledge Base of PID Controllers Tuning in Open Loop 239**  José Luis Calvo-Rolle, Ramón Ferreiro García, Antonio Couce Casanova, Héctor Quintián-Pardo

Chapter 14 **Hybrid System for Ship-Aided Design Automation 259**

**Annotated Logic for Analysis and Monitoring of the Level of Sea Water Pollutants 277**  João Inácio Da Silva Filho, Maurício C. Mário, Camilo D. Seabra Pereira, Ana Carolina Angari,

Chapter 15 **An Expert System Structured in Paraconsistent** 

Luis Fernando P. Ferrara, Odair Pitoli Jr.

Chapter 17 **An Expert System Based Approach for Diagnosis of Occurrences in Power Generating Units 327** 

Jacqueline G. Rolim and Miguel Moreto

and Dorotéa Vilanova Garcia

Vlatko Lipovac

Chapter 16 **Expert System Based Network Testing 301** 

**and Manufacturing Techniques 139**  Alejandro Quesada, Antonio Gauchía,

Chapter 9 **Expert System Used on Materials Processing 161** 

Chapter 11 **Integrated Scheduled Waste Management System in Kuala Lumpur Using Expert System 209**

Chapter 10 **Interface Layers Detection in Oil Field Tanks:** 

#### Chapter 19 **Expert System for Automatic Analysis of Results of Network Simulation 377**  Joze Mohorko, Sasa Klampfer, Matjaz Fras and Zarko Cucej

Preface

and tasks to be performed.

infrastructure.

The ability to create intelligent machines has intrigued humans since ancient times, and today with the advent of the computer and 50 years of research into AI programming techniques, the dream of smart machines is becoming a reality. Researchers are creating systems, which can mimic human beings. Accurate mathematical models neither always exist nor can they be derived for all complex environments because the domain may not be thoroughly understood. The solution consists of constructing rules that apply when

The concept of human-computer interfaces (HCI) has been undergoing changes over the years. Currently the demand is for user interfaces for ubiquitous computing. In this context, one of the basic requirements is the development of interfaces with high usability that meet different modes of interaction depending on users, environments

In carrying out the most important tasks is the lack of formalized application methods, mathematical models and advanced computer support. Decisions and adopted solutions are often based on knowledge resulting from experience and intuition of designers. Use of information on previously executed projects of similar ships allow expert systems using the Case Based Reasoning method (CBR), which is a relatively

The evolution of biological systems to adapt to their environment has fascinated and challenged scientists to increase their level of understanding of the functional characteristics of such systems. Such understanding has already benefited our society though increased life expectancy and quality, improved and cost effective health care and prevention. Engineers have looked for inspiration from such biological systems functionalities to enhance our society's communication, economic and transportation

This book has 19 chapters and explain that the expert systems are products of the artificial intelligence, branch of computer science that seeks to develop intelligent

**Petrică Vizureanu**

Romania

"Gh. Asachi" Technical University of Iasi,

new way of solving problems related to databases and knowledge bases.

input values lie within certain designer-defined categories.

programs for human, materials and automation.

## Preface

The ability to create intelligent machines has intrigued humans since ancient times, and today with the advent of the computer and 50 years of research into AI programming techniques, the dream of smart machines is becoming a reality. Researchers are creating systems, which can mimic human beings. Accurate mathematical models neither always exist nor can they be derived for all complex environments because the domain may not be thoroughly understood. The solution consists of constructing rules that apply when input values lie within certain designer-defined categories.

The concept of human-computer interfaces (HCI) has been undergoing changes over the years. Currently the demand is for user interfaces for ubiquitous computing. In this context, one of the basic requirements is the development of interfaces with high usability that meet different modes of interaction depending on users, environments and tasks to be performed.

In carrying out the most important tasks is the lack of formalized application methods, mathematical models and advanced computer support. Decisions and adopted solutions are often based on knowledge resulting from experience and intuition of designers. Use of information on previously executed projects of similar ships allow expert systems using the Case Based Reasoning method (CBR), which is a relatively new way of solving problems related to databases and knowledge bases.

The evolution of biological systems to adapt to their environment has fascinated and challenged scientists to increase their level of understanding of the functional characteristics of such systems. Such understanding has already benefited our society though increased life expectancy and quality, improved and cost effective health care and prevention. Engineers have looked for inspiration from such biological systems functionalities to enhance our society's communication, economic and transportation infrastructure.

This book has 19 chapters and explain that the expert systems are products of the artificial intelligence, branch of computer science that seeks to develop intelligent programs for human, materials and automation.

> **Petrică Vizureanu** "Gh. Asachi" Technical University of Iasi, Romania

**Part 1** 

**Human** 

**Part 1** 

**Human** 

**1** 

*University of Split,* 

*Croatia* 

**Expert System for Identification of Sport** 

Vladan Papić, Nenad Rogulj and Vladimir Pleština

**Talents: Idea, Implementation and Results** 

Selecting children for appropriate sport is the most demanding and the most responsible task for sport experts and kinesiology in general. Sport activities have significant differences regarding structural and substance features. Different sports are determined by authentic kinesiological structures and specific anthropological characteristics of an individual (Chapman, 2008; Abernethy, 2005). Success of an individual in particular sport activity is predominantly determined by the compatibility of his/her anthropological characteristics with the anthropologic model of top athletes in that sport (Morrow & James, 2005). Extensive research that has been done in order to test, analyze and compare athletes of various sports (MacDougall et al, 1991; Stergiou, 2004) brings precious information and

Unfortunately, there is usually no systematic selection in sport. The selection is based on a subjective and non-scientific judgment with a low technological and methodological support. However, fast development of new information technologies as well as the introduction of new methods and knowledge provide a novel, systematic and scientifically

In sports talent recognition process, two main problems were detected. First, task of finding an expert in this field is quite difficult due to the fact that domain of specific knowledge is separated into various sports. Also, usually experts have in-depth knowledge of the relevant factors for a specific sport and more superficial for other sports. The second problem is in fact similar with the first one and it relates to the availability of the knowledge (expert) even if we have the right person. In order to avoid this problems, the decision of developing a

Generally, knowledge acquisition techniques that are most frequently used today, require an enormous amount of time and effort on the part of both the knowledge engineer and the domain expert. They also require the knowledge engineer to have an unusually wide variety of interviewing and knowledge representation skills in order to be successful (Wagner et al., 2003). As a result, inclusion of the experts with the knowledge from both worlds, in the development of the expert system is a pre-request that should be satisfied if possible. Due to previously mentioned problem with availability of the knowledge, expert system accessibility through Internet was also required. Also, in the second version of the expert system, fuzzy logic was introduced because of detected specific issues in the evaluation process of a children or student (Papić et al., 2009). This approach is even intuitive because

knowledge that can be used for the sport talents identification, also.

based approach in selecting the appropriate sport for an individual.

computer based expert system was brought (Rogulj et al., 2006).

**1. Introduction** 

## **Expert System for Identification of Sport Talents: Idea, Implementation and Results**

Vladan Papić, Nenad Rogulj and Vladimir Pleština *University of Split, Croatia* 

## **1. Introduction**

Selecting children for appropriate sport is the most demanding and the most responsible task for sport experts and kinesiology in general. Sport activities have significant differences regarding structural and substance features. Different sports are determined by authentic kinesiological structures and specific anthropological characteristics of an individual (Chapman, 2008; Abernethy, 2005). Success of an individual in particular sport activity is predominantly determined by the compatibility of his/her anthropological characteristics with the anthropologic model of top athletes in that sport (Morrow & James, 2005). Extensive research that has been done in order to test, analyze and compare athletes of various sports (MacDougall et al, 1991; Stergiou, 2004) brings precious information and knowledge that can be used for the sport talents identification, also.

Unfortunately, there is usually no systematic selection in sport. The selection is based on a subjective and non-scientific judgment with a low technological and methodological support. However, fast development of new information technologies as well as the introduction of new methods and knowledge provide a novel, systematic and scientifically based approach in selecting the appropriate sport for an individual.

In sports talent recognition process, two main problems were detected. First, task of finding an expert in this field is quite difficult due to the fact that domain of specific knowledge is separated into various sports. Also, usually experts have in-depth knowledge of the relevant factors for a specific sport and more superficial for other sports. The second problem is in fact similar with the first one and it relates to the availability of the knowledge (expert) even if we have the right person. In order to avoid this problems, the decision of developing a computer based expert system was brought (Rogulj et al., 2006).

Generally, knowledge acquisition techniques that are most frequently used today, require an enormous amount of time and effort on the part of both the knowledge engineer and the domain expert. They also require the knowledge engineer to have an unusually wide variety of interviewing and knowledge representation skills in order to be successful (Wagner et al., 2003). As a result, inclusion of the experts with the knowledge from both worlds, in the development of the expert system is a pre-request that should be satisfied if possible. Due to previously mentioned problem with availability of the knowledge, expert system accessibility through Internet was also required. Also, in the second version of the expert system, fuzzy logic was introduced because of detected specific issues in the evaluation process of a children or student (Papić et al., 2009). This approach is even intuitive because

Expert System for Identification of Sport Talents: Idea, Implementation and Results 5

evaluation of each children/student capabilities. Thus, in order to make proposed system widely applied without any additional demands on new tests and equipment, these tests

Also, normative values for chosen tests are available from the literature (Findak et al., 1996)

As a first step, importance of each test for every sport has to be determined and stored in the knowledge base of the expert system. At this point, we have limited number of sports to 14 although using the approach that will be presented here, modular knowledge for other

Morphology Motorical Funct-

MT 3

MT 4

MT 5

MT

MT 2

ional

<sup>6</sup>FU1

were chosen as the measurement instrument for input data to our expert system.

and updated according to Norton and Olds (2001).

Fig. 1. Idea and development of the expert system.

1

lifting; MT6 - hanging endurance; FU1 - 3/6-minute running.

MO 2

M0 3

MO 4

Table 1. Example of a blank questionnaire handed to the kinesiology experts. Importance of each test has to be entered (0 - no importance, 10 - max. importance). Tests: MO1 – height; MO2 – weight; MO3 - Forearm girth; M04 - upper arm skin fold; MT1 - hand tapping; MT2 long jump from a spot; MT3 - astride touch-toe; MT4 - backward polygon; MT5 - trunk

MT 1

Sport MO

Gymnastics Swimming

Handball Football Basketball Volleyball Water polo Rowing Tennis

Athletics: sprint/jump Athletics: throwing

Martial arts: pinning Martial arts: kicking

Athletics: long dist. running

of the vagueness of expert knowledge, grades and some other data. Our approach can, in some aspects of fuzzy logic implementation, be compared to the solution proposed by Weon and Kim (2001) or the system developed for the evaluation of students' learning achievement (Bai & Chen, 2008).

The World Wide Web is reducing technological barriers and make it easier for users in different geographical locations to access the decision support models and tools (Shim et al., 2002; Bhargava et al., 2007). Internet based expert systems can have different architectures, such as centralized, replicated or distributed. This categorization is done according to the place where the code is executed (Šimić & Devedžić, 2003). Another, similar categorization (Kim, et al., 2005) of the existing methodologies is into two categories, the server-side and the client-side, depending on the location of the inference engine of a Web-enabled, rulebased system. Less burden to Web servers is present when the ASP as the server-side script approach (Wang, 2005) is used.

Review of the uses of artificial intelligence in the area of sport science and applications with focusing on introduction of expert systems as diagnostic tools for evaluating faults in sports movements has been presented in (Bartlett, 2006). The use of the expert systems for the assessment of sports talent in children have been reported in the past (Rajković et al., 1991; Leskošek et al., 1992). Some results obtained by this research were used for the development of a more specific expert system for the basketball performance prediction and assessment (Dežman et al, 2001a, 2001b). Neither of these systems have used web technologies nor implementation of fuzzy logic.

An expert system should be adaptive to constant changes of new standard values and measures as well as open to insertion of new knowledge. As already stated, first version of the expert system developed by the authors was presented in (Rogulj et al., 2006) but further development and evaluation of the system showed that there are many questions left unanswered. Improvements regarding methodology, technology and a scope of the application were done and preliminary results were presented by Papić et al. (2009). Current version of developed software based solution has the following characteristics: ability of forming a referent measurement database with the records of all potential and active sportsmen, diagnostics of their anthropological characteristics, sports talent recognition, advising and guiding amateurs into the sports activities suitable for their potential. Also, a comparison of the test results for the same person and for overall achievement monitoring through a longer time period is possible. Evaluation and tests of the presented fuzzy-based approach with some other approaches used for the evaluation of the morphology models suggest that it is capable of successful recognition of the sport compatible for the tested individual based on his/her morphological characteristics (Rogulj et al., 2009). In this chapter, detailed description of the complete system will be given along with some new results and discoveries obtained during passed time.

## **2. Idea and knowledge acquisition**

Basic idea and development steps of the expert system are presented in figure 1. It should be noted that thorough testing has to be done after each development phase. In the case of detected bugs and deficiency, previous steps should be repeated. As it can be seen from the figure 1, first four steps are relating to knowledge base forming and knowledge engineering. Basic assumptions used for this stage will be explained in the following text.

In Croatia, there is already defined set of functional, motorical and morphological tests that are mandatory for all children age 6-18 during every school year. These tests are used for the

of the vagueness of expert knowledge, grades and some other data. Our approach can, in some aspects of fuzzy logic implementation, be compared to the solution proposed by Weon and Kim (2001) or the system developed for the evaluation of students' learning

The World Wide Web is reducing technological barriers and make it easier for users in different geographical locations to access the decision support models and tools (Shim et al., 2002; Bhargava et al., 2007). Internet based expert systems can have different architectures, such as centralized, replicated or distributed. This categorization is done according to the place where the code is executed (Šimić & Devedžić, 2003). Another, similar categorization (Kim, et al., 2005) of the existing methodologies is into two categories, the server-side and the client-side, depending on the location of the inference engine of a Web-enabled, rulebased system. Less burden to Web servers is present when the ASP as the server-side script

Review of the uses of artificial intelligence in the area of sport science and applications with focusing on introduction of expert systems as diagnostic tools for evaluating faults in sports movements has been presented in (Bartlett, 2006). The use of the expert systems for the assessment of sports talent in children have been reported in the past (Rajković et al., 1991; Leskošek et al., 1992). Some results obtained by this research were used for the development of a more specific expert system for the basketball performance prediction and assessment (Dežman et al, 2001a, 2001b). Neither of these systems have used web technologies nor

An expert system should be adaptive to constant changes of new standard values and measures as well as open to insertion of new knowledge. As already stated, first version of the expert system developed by the authors was presented in (Rogulj et al., 2006) but further development and evaluation of the system showed that there are many questions left unanswered. Improvements regarding methodology, technology and a scope of the application were done and preliminary results were presented by Papić et al. (2009). Current version of developed software based solution has the following characteristics: ability of forming a referent measurement database with the records of all potential and active sportsmen, diagnostics of their anthropological characteristics, sports talent recognition, advising and guiding amateurs into the sports activities suitable for their potential. Also, a comparison of the test results for the same person and for overall achievement monitoring through a longer time period is possible. Evaluation and tests of the presented fuzzy-based approach with some other approaches used for the evaluation of the morphology models suggest that it is capable of successful recognition of the sport compatible for the tested individual based on his/her morphological characteristics (Rogulj et al., 2009). In this chapter, detailed description of the complete system will be given along with some new

Basic idea and development steps of the expert system are presented in figure 1. It should be noted that thorough testing has to be done after each development phase. In the case of detected bugs and deficiency, previous steps should be repeated. As it can be seen from the figure 1, first four steps are relating to knowledge base forming and knowledge engineering.

In Croatia, there is already defined set of functional, motorical and morphological tests that are mandatory for all children age 6-18 during every school year. These tests are used for the

Basic assumptions used for this stage will be explained in the following text.

achievement (Bai & Chen, 2008).

approach (Wang, 2005) is used.

implementation of fuzzy logic.

results and discoveries obtained during passed time.

**2. Idea and knowledge acquisition** 

evaluation of each children/student capabilities. Thus, in order to make proposed system widely applied without any additional demands on new tests and equipment, these tests were chosen as the measurement instrument for input data to our expert system.

Also, normative values for chosen tests are available from the literature (Findak et al., 1996) and updated according to Norton and Olds (2001).

Fig. 1. Idea and development of the expert system.

As a first step, importance of each test for every sport has to be determined and stored in the knowledge base of the expert system. At this point, we have limited number of sports to 14 although using the approach that will be presented here, modular knowledge for other


Table 1. Example of a blank questionnaire handed to the kinesiology experts. Importance of each test has to be entered (0 - no importance, 10 - max. importance). Tests: MO1 – height; MO2 – weight; MO3 - Forearm girth; M04 - upper arm skin fold; MT1 - hand tapping; MT2 long jump from a spot; MT3 - astride touch-toe; MT4 - backward polygon; MT5 - trunk lifting; MT6 - hanging endurance; FU1 - 3/6-minute running.

Expert System for Identification of Sport Talents: Idea, Implementation and Results 7

Fig. 2. Membership functions of the fuzzy sets "short", "medium" and "tall" used for the

Fig. 3. Membership functions of the fuzzy sets "very low", "low", "semi-low", "semi-high", "high" and "very high" used for the calculation of fuzzy membership grade for BMI.

*FH*

Fuzzy grade vector for BMI (FB) can be presented as follows:

"semi-high", "high" and "very high", respectively, whereas

value of the BMI belonging to the linguistic term *FBi*, 0,1 , 1 6 *BMIi*

Fuzzification of the measured height and calculated BMI has been done according to the fuzzy sets presented in Figs. 2 and 3. Fuzzy grade vector for height (*FH*) can be presented as

where *FH1*, *FH2*, *FH3* denote the fuzzy terms "short", "medium" and "tall", respectively, whereas *μhi* denote the membership value of the height belonging to the linguistic term *FHi*,

123 *hhh* 123 *FH FH FH*

123456 *BMI BMI BMI BMI BMI BMI* 123456 *FB FB FB FB FB FB*

μ

μ

∈ ≤≤ ⎡ ⎤ *i* ⎣ ⎦ .

*BMIi* denote the membership

μμμμμμ

where *FB1*, *FB2*, *FB3 FB4*, *FB5* and *FB6* denote the fuzzy terms "very low", "low", "semi-low",

<sup>⎡</sup> <sup>⎤</sup> <sup>=</sup> <sup>⎢</sup> <sup>⎥</sup> <sup>⎣</sup> <sup>⎦</sup>

μμμ

<sup>⎡</sup> <sup>⎤</sup> <sup>=</sup> <sup>⎢</sup> <sup>⎥</sup> <sup>⎣</sup> <sup>⎦</sup>

calculation of fuzzy membership grade for height.

follows:

μ

0,1 , 1 3 *hi*

∈ ≤≤ ⎡ ⎤ *i* ⎣ ⎦ .

*FB*

sports can easily be added to the knowledge base. Determination of the tests importance was based on the expert knowledge obtained from 97 kinesiology experts. A questionnaire presented by Table 1 was prepared and handed out to two groups of experts: general knowledge experts (kinesiology teachers in high and elementary schools) and experts in a particular sport (trainers and university professors).

Each expert had to fill the table with an integer importance factor from the interval [0,10] where 10 represents highest importance. Because of different scopes and depths of expert's knowledge, extensive data processing and adaptation of acquired knowledge was done after the answers to the questionnaire were given. An expert in the particular sport had to rate the importance of each test evaluating only the sport of his/her expertise while general knowledge experts evaluated test importance for all the sports. Test weight factors obtained by experts for particular sport (47 experts) have significantly more importance than test weight factors obtained by the general knowledge experts (52 experts), but the latter group's results were used as a correction factor because their accumulated knowledge provided more clear "big picture" than only partial image brought by the first group.

## **3. Knowledge processing**

In this section calculation procedure for the person's adequacy for fourteen chosen sports will be explained in detail. Although in first implementation attempts fuzzy logic wasn't used, preliminary results have shown that fuzzy reasoning should be introduced for some specific tests.

#### **3.1 Calculation of body fitness using fuzzy logic**

Sport activities differ to a large extent in structure and content. Different sports are characterized by authentic kinesiological structures and specific anthropological features. The success of an individual in a certain sport activity depends mostly on the compatibility of his anthropological features, or the so-called anthropological model for the given sport (Katić et al., 2005). Therefore, in evaluation process, it is crucial to detect persons whose anthropological features match specific qualities of a certain kinesiological activity.

Measurements obtained by height and weight tests are used together in order to obtain body fitness for the particular sport. In kinesiology, this is an issue known as athletic body and this feature has its own membership grade instead of two separate ones for body weight and height. Importance factor of the indirect test equals sum of their individual weights. Evaluation of the tested person's body fitness for the particular sport is calculated using the rules with implemented fuzzy logic. In fact, athletic body of a person is represented by person's height and body mass index (BMI), so BMI, has to be calculated from height and weight of a person using the following equation:

$$BMI = \frac{w}{h^2} \tag{1}$$

where *w* is weight and *h* is height of a person.

After the analysis of the results from the filled and returned questionnaires and also with the comparison of the available national teams' anthropometric data, models of the ideal height and BMI were included into the expert system database.

sports can easily be added to the knowledge base. Determination of the tests importance was based on the expert knowledge obtained from 97 kinesiology experts. A questionnaire presented by Table 1 was prepared and handed out to two groups of experts: general knowledge experts (kinesiology teachers in high and elementary schools) and experts in a

Each expert had to fill the table with an integer importance factor from the interval [0,10] where 10 represents highest importance. Because of different scopes and depths of expert's knowledge, extensive data processing and adaptation of acquired knowledge was done after the answers to the questionnaire were given. An expert in the particular sport had to rate the importance of each test evaluating only the sport of his/her expertise while general knowledge experts evaluated test importance for all the sports. Test weight factors obtained by experts for particular sport (47 experts) have significantly more importance than test weight factors obtained by the general knowledge experts (52 experts), but the latter group's results were used as a correction factor because their accumulated knowledge provided

In this section calculation procedure for the person's adequacy for fourteen chosen sports will be explained in detail. Although in first implementation attempts fuzzy logic wasn't used, preliminary results have shown that fuzzy reasoning should be introduced for some

Sport activities differ to a large extent in structure and content. Different sports are characterized by authentic kinesiological structures and specific anthropological features. The success of an individual in a certain sport activity depends mostly on the compatibility of his anthropological features, or the so-called anthropological model for the given sport (Katić et al., 2005). Therefore, in evaluation process, it is crucial to detect persons whose anthropological features match specific qualities of a certain kinesiological

Measurements obtained by height and weight tests are used together in order to obtain body fitness for the particular sport. In kinesiology, this is an issue known as athletic body and this feature has its own membership grade instead of two separate ones for body weight and height. Importance factor of the indirect test equals sum of their individual weights. Evaluation of the tested person's body fitness for the particular sport is calculated using the rules with implemented fuzzy logic. In fact, athletic body of a person is represented by person's height and body mass index (BMI), so BMI, has to be calculated from height and

> 2 *<sup>w</sup> BMI*

After the analysis of the results from the filled and returned questionnaires and also with the comparison of the available national teams' anthropometric data, models of the ideal

*<sup>h</sup>* <sup>=</sup> (1)

more clear "big picture" than only partial image brought by the first group.

particular sport (trainers and university professors).

**3.1 Calculation of body fitness using fuzzy logic** 

weight of a person using the following equation:

where *w* is weight and *h* is height of a person.

height and BMI were included into the expert system database.

**3. Knowledge processing** 

specific tests.

activity.

Fig. 2. Membership functions of the fuzzy sets "short", "medium" and "tall" used for the calculation of fuzzy membership grade for height.

Fig. 3. Membership functions of the fuzzy sets "very low", "low", "semi-low", "semi-high", "high" and "very high" used for the calculation of fuzzy membership grade for BMI.

Fuzzification of the measured height and calculated BMI has been done according to the fuzzy sets presented in Figs. 2 and 3. Fuzzy grade vector for height (*FH*) can be presented as follows:

$$\begin{array}{rcl} FH &=& \begin{bmatrix} FH\_1 & FH\_2 & FH\_3 \\ \mu\_{h1} & \mu\_{h2} & \mu\_{h3} \end{bmatrix} \end{array}$$

where *FH1*, *FH2*, *FH3* denote the fuzzy terms "short", "medium" and "tall", respectively, whereas *μhi* denote the membership value of the height belonging to the linguistic term *FHi*, 0,1 , 1 3 *hi* μ∈ ≤≤ ⎡ ⎤ *i* ⎣ ⎦ .

Fuzzy grade vector for BMI (FB) can be presented as follows:

$$\begin{array}{cccccccc} \text{ } & FB & = & \begin{bmatrix} FB\_1 & FB\_2 & FB\_3 & FB\_4 & FB\_5 & FB\_6\\ \mu\_{\text{BMI}1} & \mu\_{\text{BMI}2} & \mu\_{\text{BMI}3} & \mu\_{\text{BMI}4} & \mu\_{\text{BMI}5} & \mu\_{\text{BMI}6} \end{bmatrix} \end{array}$$

where *FB1*, *FB2*, *FB3 FB4*, *FB5* and *FB6* denote the fuzzy terms "very low", "low", "semi-low", "semi-high", "high" and "very high", respectively, whereas μ *BMIi* denote the membership value of the BMI belonging to the linguistic term *FBi*, 0,1 , 1 6 *BMIi* μ∈ ≤≤ ⎡ ⎤ *i* ⎣ ⎦ .

Expert System for Identification of Sport Talents: Idea, Implementation and Results 9

{ () () ()} ' '' '' '' ,1 ,2 , , ,...

where *N* is a total number of rules that as an output have membership grade of the linguistic value *Mj*. Finally, the athletic body membership grade of the observed individual for

> ( ) ( ) ' ' 2 3 *M k* 0.5 , .

Now, complete procedure for calculation of person's fitness for particular sport will be

where *SK* denotes the *k*-th sport in *S* and 1 ≤ ≤ *K p* . Now, let's assume that there is a series of

where *Gi* denotes the *i*-th test group in *G* and 1 ≤ ≤*i n* . Assume that test group *Gi* consists of *m* tests *Ti1*, *Ti2*,…, *Tim*. We can define the input vector with the elements representing the

Next, the contribution of the test group *Gi* for the evaluation of a person's fitness for a

( ) ( ) ( ) ( ) \*

the test *Tij* for a particular sport *SK*, ∑ denotes the algebraic sum and × denotes the algebraic product. Note: membership grades for height and weight tests are substituted with the

μ

fitness index (TFI) for sport *SK* is calculated as the algebraic sum of test group contributions:

*n K Si i TFI S C G* =

As it can be noticed, in order to compare TFI for different sports, normalization of weight factors has to be done. Normalization assumes that the maximum fitness index (MFI) that

<sup>1</sup> *<sup>K</sup>*

*ij* denotes the membership grade of the test *Tij*, *w S ij*( ) *<sup>K</sup>* denotes weight factor of

μ

11 12 1 21 2 *<sup>T</sup> RR R R R R R* <sup>=</sup> *<sup>n</sup> n mn* <sup>⎡</sup> <sup>⎤</sup> <sup>⎣</sup> " "" <sup>⎦</sup>

*m m S i S ij ij ij K j j C G C T wS*

= =

1 1 *K K*

athletic body membership grade calculated according to equation (4).

μ

If the value of the membership grade is 0 ( \* 0

( ) ( )

maximal membership grade value ( \* 1

measurement result *Rij* for each conducted test T*ij* of the observed individual:

μ μ

*S Max* = ×

 μ*ij* = *Max M M M M j M j MN j* (3)

*k k* (4)

1 2 , ,..., *S SS S* = *<sup>p</sup>* (5)

1 2 , ,..., *G GG G* = *<sup>n</sup>* (6)

= =× ∑ ∑ (7)

*ij* = ), then the test *Tij* result was poor, and

<sup>=</sup> ∑ (8)

*ij* = ) means that the test *Tij* result was excellent. Total

 μ

μμ

μ

Assume that there is a series of sports *S1*, *S2*, …, *Sp* in sports domain *S*,

**3.2 Calculation of the total fitness for particular sport** 

test groups *G1*, *G2*, …, *Gn* in test group domain *G*,

particular sport (*SK*) is defined as:

where \* μ

particular sport is calculated as follows:

explained in details.

An example of a fuzzy rule matrix to infer the body model adequacy is presented in Table 1. Each sport has different rule matrix.

Based on the fuzzy grade vectors *FH*, *FB* and fuzzy rules which are partially shown in Table 2, fuzzy reasoning is performed in order to evaluate the athletic body adequacy for each sport.


Table 2. Fuzzy rule matrix for sport *Sk*. Possible linguistic values for a*i,j*(*Sk*) are: unmatched, semi-matched, matched.

Generally, we can write a fuzzy rule as follows:

IF the sport is *Sk* and the height is *FHi* and BMI is *FBj* THEN model is Ml

where Ml can have three linguistic values: M1 = "unmatched", M2 = "semi-matched" and M3 = "matched".

The triggering of each rule as a result gives the model membership grade. Linguistic value (Ml) in the consequent part of the rule determines which linguistic variable the membership grade relates to. Result of each rule is calculated as follows:

$$
\mu\_M^\circ(M\_l) = w\_H \left( S\_k \right) \times \mu\_{Hi} + w\_{BMl} \left( S\_k \right) \times \mu\_{BMlj} \tag{2}
$$

where *w S H K* ( ) and ( ) *w S BMI K* denotes weight factor of the height and BMI test for a particular sport *Sk*, and Ml is the linguistic value in the consequent part of the rule. Other linguistic variables , *M j <sup>j</sup>* ≠ *l* are not affected on the rule and their membership grades are zero.

Because of the simplicity, in the equation (2), sport verification is left out from the antecedent part of the rule. In fact, in the expert system database, rules are grouped by sports and only rules related to the particular sport will be fired. Model matrix (M) used for calculation of body model membership *μM* for each sport (S*1*, …, S*P*) is obtained after the triggering of all the fuzzy rules and the aggregation of their output for each linguistic value M1, M2 and M3 by using the Max() function.

Matrix elements ' ' 11 3 ,..., μ μ*<sup>p</sup>* are fuzzy values obtained by evaluation of fuzzy rules.

$$\begin{array}{c c c c c} & M\_1 & M\_2 & M\_3 \\ \mathbf{S}\_1 & \begin{bmatrix} \mu\_{11}^{\cdot} & \mu\_{12}^{\cdot} & \mu\_{13}^{\cdot} \\ \mu\_{21}^{\cdot} & \mu\_{22}^{\cdot} & \mu\_{23}^{\cdot} \\ \vdots & \vdots & \vdots \\ \mu\_{p1}^{\cdot} & \mu\_{p2}^{\cdot} & \mu\_{p3}^{\cdot} \end{bmatrix} \\ \mathbf{S}\_p & \begin{bmatrix} \mu\_{p1}^{\cdot} & \mu\_{p2}^{\cdot} & \mu\_{p3}^{\cdot} \end{bmatrix} \end{array}$$

Each element ' μ*ij* is calculated according to fuzzy rules as follows:

{ () () ()} ' '' '' '' ,1 ,2 , , ,... μμ μ μ*ij* = *Max M M M M j M j MN j* (3)

where *N* is a total number of rules that as an output have membership grade of the linguistic value *Mj*. Finally, the athletic body membership grade of the observed individual for particular sport is calculated as follows:

$$
\mu\_M(S\_k) = \text{Max}\{0.5 \times \mu\_{k2}^\circ, \mu\_{k3}^\circ\}. \tag{4}
$$

#### **3.2 Calculation of the total fitness for particular sport**

8 Expert Systems for Human, Materials and Automation

An example of a fuzzy rule matrix to infer the body model adequacy is presented in Table 1.

Based on the fuzzy grade vectors *FH*, *FB* and fuzzy rules which are partially shown in Table 2, fuzzy reasoning is performed in order to evaluate the athletic body adequacy for each

Height Very low Low Semi-low Semi-high High Very

Short *a1,1*(*Sk*) *a2,1*(*Sk*) *a3,1*(*Sk*) *a4,1*(*Sk*) *a5,1*(*Sk*) *a6,1*(*Sk*) Medium *a1,2*(*Sk*) *a2,2*(*Sk*) *a3,2*(*Sk*) *a4,2*(*Sk*) *a5,2*(*Sk*) *a6,2*(*Sk*) Tall *a1,3*(*Sk*) *a2,3*(*Sk*) *a3,3*(*Sk*) *a4,3*(*Sk*) *a5,3*(*Sk*) *a6,3*(*Sk*) Table 2. Fuzzy rule matrix for sport *Sk*. Possible linguistic values for a*i,j*(*Sk*) are: unmatched,

where Ml can have three linguistic values: M1 = "unmatched", M2 = "semi-matched" and

The triggering of each rule as a result gives the model membership grade. Linguistic value (Ml) in the consequent part of the rule determines which linguistic variable the membership

() () ''

variables , *M j <sup>j</sup>* ≠ *l* are not affected on the rule and their membership grades are zero.

*S M S*

*S*

*ij* is calculated according to fuzzy rules as follows:

where *w S H K* ( ) and ( ) *w S BMI K* denotes weight factor of the height and BMI test for a particular sport *Sk*, and Ml is the linguistic value in the consequent part of the rule. Other linguistic

Because of the simplicity, in the equation (2), sport verification is left out from the antecedent part of the rule. In fact, in the expert system database, rules are grouped by sports and only rules related to the particular sport will be fired. Model matrix (M) used for calculation of body model membership *μM* for each sport (S*1*, …, S*P*) is obtained after the triggering of all the fuzzy rules and the aggregation of their output for each linguistic value

μμ

*<sup>p</sup>* are fuzzy values obtained by evaluation of fuzzy rules.

123 '''

*MMM*

μμμ

<sup>⎡</sup> <sup>⎤</sup> <sup>⎢</sup> <sup>⎥</sup>

μμμ

1 11 12 13 '''

2 21 22 23

<sup>=</sup> <sup>⎢</sup> <sup>⎥</sup> <sup>⎢</sup> <sup>⎥</sup> <sup>⎢</sup> <sup>⎥</sup> <sup>⎢</sup> <sup>⎥</sup> ⎢⎣ ⎥⎦ # ###

<sup>123</sup> *<sup>p</sup> ppp*

'''

μμμ

*M wS w S l H k Hi BMI k BMIj* = ×+ × (2)

Body mass index (BMI)

high

Each sport has different rule matrix.

semi-matched, matched.

M3 = "matched".

Generally, we can write a fuzzy rule as follows:

M1, M2 and M3 by using the Max() function.

11 3 ,...,

μ

μ

Matrix elements ' '

Each element '

μ

IF the sport is *Sk* and the height is *FHi* and BMI is *FBj* THEN model is Ml

grade relates to. Result of each rule is calculated as follows:

*<sup>M</sup>*( ) μ

sport.

Now, complete procedure for calculation of person's fitness for particular sport will be explained in details.

Assume that there is a series of sports *S1*, *S2*, …, *Sp* in sports domain *S*,

$$S = S\_1 \, S\_2 \, \dots \, S\_p \tag{5}$$

where *SK* denotes the *k*-th sport in *S* and 1 ≤ ≤ *K p* . Now, let's assume that there is a series of test groups *G1*, *G2*, …, *Gn* in test group domain *G*,

$$G = G\_1, G\_2, \dots, G\_n \tag{6}$$

where *Gi* denotes the *i*-th test group in *G* and 1 ≤ ≤*i n* . Assume that test group *Gi* consists of *m* tests *Ti1*, *Ti2*,…, *Tim*. We can define the input vector with the elements representing the measurement result *Rij* for each conducted test T*ij* of the observed individual:

$$R = \begin{bmatrix} R\_{11} & R\_{12} & \cdots & R\_{1n} & R\_{21} & \cdots & R\_{2n} & \cdots & R\_{mn} \end{bmatrix}^T$$

Next, the contribution of the test group *Gi* for the evaluation of a person's fitness for a particular sport (*SK*) is defined as:

$$\mathbf{C}\_{S\_K} \left( \mathbf{G}\_i \right) = \sum\_{j=1}^{m} \mathbf{C}\_{S\_K} \left( T\_{ij} \right) = \sum\_{j=1}^{m} \left( \mu\_{ij}^\* \times w\_{ij} \left( \mathbf{S}\_K \right) \right) \tag{7}$$

where \* μ*ij* denotes the membership grade of the test *Tij*, *w S ij*( ) *<sup>K</sup>* denotes weight factor of the test *Tij* for a particular sport *SK*, ∑ denotes the algebraic sum and × denotes the algebraic product. Note: membership grades for height and weight tests are substituted with the athletic body membership grade calculated according to equation (4).

If the value of the membership grade is 0 ( \* 0 μ*ij* = ), then the test *Tij* result was poor, and maximal membership grade value ( \* 1 μ*ij* = ) means that the test *Tij* result was excellent. Total fitness index (TFI) for sport *SK* is calculated as the algebraic sum of test group contributions:

$$TFI\{S\_K\} = \sum\_{i=1}^{n} \mathbb{C}\_{S\_K} \left(G\_i\right) \tag{8}$$

As it can be noticed, in order to compare TFI for different sports, normalization of weight factors has to be done. Normalization assumes that the maximum fitness index (MFI) that

Expert System for Identification of Sport Talents: Idea, Implementation and Results 11

normative class lower boundary value; *k l*, 1 *c* <sup>+</sup> is the upper boundary of normative class

interpolation of normative classes and corresponding grades is done. In fact, two rules are fired – one with the nearest lower age in the antecedent part of the rule and another with the nearest upper age in the antecedent part of the rule. Final membership grade value can be

( ) ( )

+

1 ,1 1, 1 , 1

, 1, , 1 1, 1 1 ;

*kl k l l kl k l l* == = = <sup>+</sup> + ++ + .

*l kl k l kl*

( ) \* \* <sup>1</sup> \* \* 1 *l l*

 μ

*ij ij l l*

Although entity names presented in Fig. 5 are descriptive and may differ to the table names

Fig. 5. Expert system structure. Expert knowledge is stored as rules, norms and test weights

+

+ + ++ +

*c c kc c*

κ

 μμ

*l l*

*c c* μ

in the database, structure that is presented gives the main relations between them.

+

\* , 1, ,

*c c kc c*

= + −⋅ −

κ

Membership grade indexes for particular age value can be simplified:

 μ

μ

*l kl k l kl*

( ) ( )

 μ

*R c*

*k l*, 1+ is membership grade for the normative class

) is generally not an integer number (in years), an

= + −⋅ − (12)

 μ

<sup>−</sup> = ⋅ −+ − (13)

 μ

μ

κ

which includes measured value, and

Because the age of the tested person (

calculated using the following equations:

**4. Implementation and development** 

\*

μ

upper boundary value.

Finally,

for each sport.

can be obtained for each sport is equal which means that the following condition must be satisfied

$$MFI\left(S\_K\right) = \sum\_{i=1}^{n} M\_{S\_K}\left(G\_i\right) = 1, \qquad \forall S\_K \in S \tag{9}$$

where maximum possible contribution of *i*-th test group for sport *SK* is given by equation:

$$\mathcal{M}\_{\mathcal{S}\_K} \left( \mathcal{G}\_i \right) = \sum\_{j=1}^m w\_{ij} \left( \mathcal{S}\_K \right) \tag{10}$$

Membership grade \* μ*ij* of the test *Tij* needed for the equation (7) is calculated using the available test normative data for a particular gender and age. Each normative class (*cl*) is defined by its minimal (*n1*) and maximal value (*n2*) and it can be expressed with the rule in the following form:

$$\text{IF}\{\text{test}\,=T\_{\bar{\eta}/\prime}\text{ gender}\,=X,\text{age}\,=k\}\textbf{THEN}\{c\_{l,\text{min}}=n\_1; c\_{l,\text{max}}=n\_2\}$$

where *cl,min* and *cl,max* are the lower and upper boundary of the normative class *l*, respectively. Normative classes boundaries are directly associated with discrete membership grade values (Fig. 4).

Fig. 4. Membership grade μ*ij* of the test *Tij* as a function of test normative classes for particular age (and gender).

For the measured or induced (in the case of height and BMI measurements) result (*Rij*) of the test (*Tij*), membership grade can be calculated using the equation

$$\mu\_k = \frac{\mu\_{k,l+1} - \mu\_{k,l}}{c\_{k,l+1} - c\_{k,l}} \cdot \left(R\_{ij} - c\_{k,l}\right) + \mu\_{k,l} \qquad ; R\_{ij} \in \left(c\_{k,l}, c\_{k,l+1}\right] \tag{11}$$

where *k* is age of the tested person (integer value), *k l*, *c* is the lower boundary of the normative class which includes measured value, and μ*k l*, is a membership grade for the normative class lower boundary value; *k l*, 1 *c* <sup>+</sup> is the upper boundary of normative class which includes measured value, and μ*k l*, 1+ is membership grade for the normative class upper boundary value.

Because the age of the tested person (κ ) is generally not an integer number (in years), an interpolation of normative classes and corresponding grades is done. In fact, two rules are fired – one with the nearest lower age in the antecedent part of the rule and another with the nearest upper age in the antecedent part of the rule. Final membership grade value can be calculated using the following equations:

$$\begin{aligned} \mathbf{c}\_{l}^{\*} &= \mathbf{c}\_{k,l} + (\kappa - k) \cdot \left( \mathbf{c}\_{k+1,l} - \mathbf{c}\_{k,l} \right) \\ \mathbf{c}\_{l+1}^{\*} &= \mathbf{c}\_{k,l+1} + (\kappa - k) \cdot \left( \mathbf{c}\_{k+1,l+1} - \mathbf{c}\_{k,l+1} \right) \end{aligned} \tag{12}$$

Membership grade indexes for particular age value can be simplified:

$$
\mu\_{k,l} = \mu\_{k+1,l} = \mu\_l ; \mu\_{k,l+1} = \mu\_{k+1,l+1} = \mu\_{l+1} \dots
$$

Finally,

10 Expert Systems for Human, Materials and Automation

can be obtained for each sport is equal which means that the following condition must be

1, *<sup>K</sup>*

( ) ( )

*ij* of the test *Tij* needed for the equation (7) is calculated using the

*ij* of the test *Tij* as a function of test normative classes for

; ,

<sup>⎦</sup> <sup>−</sup> (11)

*k l*, is a membership grade for the

For the measured or induced (in the case of height and BMI measurements) result (*Rij*) of the

*k ij k l k l ij k l k l*

where *k* is age of the tested person (integer value), *k l*, *c* is the lower boundary of the

( ) ( ,1 , , , , ,1

<sup>−</sup> = ⋅− + ∈ <sup>⎤</sup>

 μ<sup>+</sup> <sup>+</sup>

*Rc R cc*

μ

*m Si ij K j M G wS* =

available test normative data for a particular gender and age. Each normative class (*cl*) is defined by its minimal (*n1*) and maximal value (*n2*) and it can be expressed with the rule in

( ) ( ) , 1, 2 test , gender , age ; = == = = *T X k c nc n ij l min l max* **IF THEN**

where *cl,min* and *cl,max* are the lower and upper boundary of the normative class *l*, respectively. Normative classes boundaries are directly associated with discrete

= = ∀∈ ∑ (9)

<sup>=</sup> ∑ (10)

*K Si K*

*MFI S M G S S*

where maximum possible contribution of *i*-th test group for sport *SK* is given by equation:

<sup>1</sup> *<sup>K</sup>*

( ) ( ) 1

*n*

*i*

=

satisfied

Membership grade \*

the following form:

μ

membership grade values (Fig. 4).

Fig. 4. Membership grade

particular age (and gender).

μ

test (*Tij*), membership grade can be calculated using the equation

 μ

,1 ,

*kl kl*

*c c*

+

normative class which includes measured value, and

μ

μ

*kl kl*

$$
\mu\_{ij}^\* = \frac{\mu\_{l+1} - \mu\_l}{c\_{l+1}^\* - c\_l^\*} \cdot \left( R\_{ij} - c\_l^\* \right) + \mu\_l \tag{13}
$$

#### **4. Implementation and development**

Although entity names presented in Fig. 5 are descriptive and may differ to the table names in the database, structure that is presented gives the main relations between them.

Fig. 5. Expert system structure. Expert knowledge is stored as rules, norms and test weights for each sport.

Expert System for Identification of Sport Talents: Idea, Implementation and Results 13

Within last year, quantitative contributions of certain motor abilities to the potential dance efficiency through expert knowledge were determined. Good metrical characteristics of the expert knowledge were determined, and after the experimental implementation of the results of research into the system, fine prognostic efficiency in recognising individuals

Typical output of the presented system consists of calculated percentages that are corresponding to the adequacy of the examinee for each sport that has needed data (norms,

In order to evaluate objectivity of the normative values and test weights stored in the knowledge base, average results for group of 106 examinees (45 female, 61 male) of various ages were analysed (Table 3). Combined results for both groups (female and male) are

Differences obtained between sports are generally small except maybe athletics – long distance running. This is indicating to unbalanced tests for this sport. In fact, this could be expected because of only one functional test in the tests battery. Also, almost 4% average differences between males and females indicate possible deviations of the present normative values.

engaged in dance activities was established (Srhoj, Lj. Et al., 2010).

test weights) stored in the knowledge base (Fig. 7).

Fig. 7. Typical output of the expert system

presented in Table 4.

**5. Results and analysis** 

Knowledge engineering, forming of the knowledge base and coding of the stand-alone application lasted for about 12 months. After testing phase that lasted for about 3 months, fuzzy logic was introduced into the measurement evaluation and the migration of the code to the web application was done.

Web version of Sport Talent is built on a Microsoft asp.net platform with Borland Delphi 2005 as asp.net application. Application database is Microsoft SQL server 2000 which is connected with Sport Talent application using SQLConnection component (Fig. 6).

The application consists of files with aspx extension made available via http using the Internet Information Service as web server. These files are containing both HTML and server-side code which is written in object pascal. HTML and server-side code is combined in order to create the final output of the page consisting of HTML markup that is sent to the client browser. User controls i.e. fully programmable objects (both code and presentation layer) of the asp.net (.ascx) web page were also done to provide full functionality of the application.

Fig. 6. Web server with application and user connection.

Since beginning of 2008, web version of the system along with the fuzzy module has been mounted on the web server. Chosen group of experts and school teachers has used the application since then and the database is growing daily.

Output generated by the expert system was compared with answers obtained by the human users and, in second test, prediction of the system based on the measurements of the successful athletes that are collected several years before they achieved elite level in sport. System evaluation results showed high reliability and high correlation with top experts in the field and the results for the second test also showed good match (Papić et al., 2009).

Within last year, quantitative contributions of certain motor abilities to the potential dance efficiency through expert knowledge were determined. Good metrical characteristics of the expert knowledge were determined, and after the experimental implementation of the results of research into the system, fine prognostic efficiency in recognising individuals engaged in dance activities was established (Srhoj, Lj. Et al., 2010).

## **5. Results and analysis**

12 Expert Systems for Human, Materials and Automation

Knowledge engineering, forming of the knowledge base and coding of the stand-alone application lasted for about 12 months. After testing phase that lasted for about 3 months, fuzzy logic was introduced into the measurement evaluation and the migration of the code

Web version of Sport Talent is built on a Microsoft asp.net platform with Borland Delphi 2005 as asp.net application. Application database is Microsoft SQL server 2000 which is

The application consists of files with aspx extension made available via http using the Internet Information Service as web server. These files are containing both HTML and server-side code which is written in object pascal. HTML and server-side code is combined in order to create the final output of the page consisting of HTML markup that is sent to the client browser. User controls i.e. fully programmable objects (both code and presentation layer) of the asp.net

> User computer with Web browser

Since beginning of 2008, web version of the system along with the fuzzy module has been mounted on the web server. Chosen group of experts and school teachers has used the

Output generated by the expert system was compared with answers obtained by the human users and, in second test, prediction of the system based on the measurements of the successful athletes that are collected several years before they achieved elite level in sport. System evaluation results showed high reliability and high correlation with top experts in the field and the results for the second test also showed good match (Papić et al., 2009).

with Web browser

Fig. 6. Web server with application and user connection.

application since then and the database is growing daily.

User computer with Web browser

**Internet**

Sport Talent Web application (ASP.NET)

User computer

connected with Sport Talent application using SQLConnection component (Fig. 6).

(.ascx) web page were also done to provide full functionality of the application. Expert system – sport talent

> + translation

MS SQL Database - measurements database - expert

knowledge Processing

to the web application was done.

Typical output of the presented system consists of calculated percentages that are corresponding to the adequacy of the examinee for each sport that has needed data (norms, test weights) stored in the knowledge base (Fig. 7).


Fig. 7. Typical output of the expert system

In order to evaluate objectivity of the normative values and test weights stored in the knowledge base, average results for group of 106 examinees (45 female, 61 male) of various ages were analysed (Table 3). Combined results for both groups (female and male) are presented in Table 4.

Differences obtained between sports are generally small except maybe athletics – long distance running. This is indicating to unbalanced tests for this sport. In fact, this could be expected because of only one functional test in the tests battery. Also, almost 4% average differences between males and females indicate possible deviations of the present normative values.

Expert System for Identification of Sport Talents: Idea, Implementation and Results 15

base is the result of the knowledge acquired from 97 kinesiology experts. System evaluation results that were conducted during testing phase of the system showed high reliability and

At present, measurements database has several hundreds measured children of various ages (primary and secondary schools) so updating of the normative data for the currently active tests is possible. Authors expect that it would further improve prediction reliability. It should be accented that presented system allows real time insight into the current

As the consequence of using this system, the possibility of wrong selection and losing several years in training of an inappropriate sport should be significantly reduced. Other benefits are: proper use of the anthropometric potential of a sportsman, fewer frustrations due to poor performance, achievement of the top results in sport and improved efficiency of

At the moment, the system stores normative data and weight factors information on fourteen sports. Recent research includes adding other sports into the domain of the presented expert system. First sport that is expected to be added is dance. Also, some sports such as basketball and athletics should be separated into new entities according to player's position (basketball) or specialization (athletics). Generation of output reports for the users are also part of the current work. Our intention is to make the reports more users friendly and to avoid output results in the terms of percentages. Automatic generation of linguistically rich and visually attractive report is expected to be more adequate for the users. Perhaps the most important issue that we are currently dealing with is the establishing new set of standard tests that are

Present configuration is modular and that makes implementation of various modifications quite simple i.e. without the need to make some structural changes that could take time and would make the expert system unavailable for a longer period. As the authors see it, the main goal of this research is to make using this system mandatory to all school teachers and to allow trainers of various sports to have access to the measurement results as well. Only

This work was supported by the Ministry of Science and Technology of the Republic Croatia

Abernethy, B. (2005). *Biophysical Foundations of Human Movement*. 2nd Edition, Human

Bai, S. M.; & Chen, S. M. (2008). Evaluating students' learning achievement using fuzzy

Bartlett, R. (2006). Artificial intelligence in sports biomechanics: New dawn or false hope?

Bhargava, H. K.; Power, D. J. & Sun, D. (2007). Progress in Web-based decision support

Chapman, A. (2008). *Biomechanical Analysis of Fundamental Human Movements*. Human

membership functions and fuzzy rules. *Expert Systems with Applications*, 34, 399–

correlation with top experts in the field.

anthropometric measures of the examinees.

expected to have better metric characteristics than present one.

under projects: 177-0232006-1662 and 177-0000000-1811.

*Journal of Sports Science and Medicine*, 5, 474-479.

technologies. *Decision Support Systems*, 43, 1083–1095.

Kinetics, Champaign.

Kinetics, Champaign.

then, benefits of this expert system could be used up to its full potential.

spending finances.

**7. Acknowledgment** 

**8. References** 

410.


Table 3. Average output results for 106 examinees, female and male separately.


Table 4. Average output results for all examinees.

## **6. Conclusion and discussion**

In this chapter we have presented an expert system for the selection and identification of an optimal sport for a child. This is the first expert system developed for this purpose that uses fuzzy logic and has wide Internet accessibility. Expert knowledge stored in the knowledge base is the result of the knowledge acquired from 97 kinesiology experts. System evaluation results that were conducted during testing phase of the system showed high reliability and correlation with top experts in the field.

At present, measurements database has several hundreds measured children of various ages (primary and secondary schools) so updating of the normative data for the currently active tests is possible. Authors expect that it would further improve prediction reliability. It should be accented that presented system allows real time insight into the current anthropometric measures of the examinees.

As the consequence of using this system, the possibility of wrong selection and losing several years in training of an inappropriate sport should be significantly reduced. Other benefits are: proper use of the anthropometric potential of a sportsman, fewer frustrations due to poor performance, achievement of the top results in sport and improved efficiency of spending finances.

At the moment, the system stores normative data and weight factors information on fourteen sports. Recent research includes adding other sports into the domain of the presented expert system. First sport that is expected to be added is dance. Also, some sports such as basketball and athletics should be separated into new entities according to player's position (basketball) or specialization (athletics). Generation of output reports for the users are also part of the current work. Our intention is to make the reports more users friendly and to avoid output results in the terms of percentages. Automatic generation of linguistically rich and visually attractive report is expected to be more adequate for the users. Perhaps the most important issue that we are currently dealing with is the establishing new set of standard tests that are expected to have better metric characteristics than present one.

Present configuration is modular and that makes implementation of various modifications quite simple i.e. without the need to make some structural changes that could take time and would make the expert system unavailable for a longer period. As the authors see it, the main goal of this research is to make using this system mandatory to all school teachers and to allow trainers of various sports to have access to the measurement results as well. Only then, benefits of this expert system could be used up to its full potential.

## **7. Acknowledgment**

This work was supported by the Ministry of Science and Technology of the Republic Croatia under projects: 177-0232006-1662 and 177-0000000-1811.

## **8. References**

14 Expert Systems for Human, Materials and Automation

Athletics – long dist. running 60,50 Athletics – long dist. running 52,57 Martial arts – kicking 55,44 Athletics – sprint/jump 49,90 Athletics – sprint/jump 55,11 Martial arts – kicking 49,08 Football 49,94 Football 45,75 Tennis 46,44 Tennis 42,85 Martial arts – push/pull 45,33 Martial arts – push/pull 40,82 Gymnastics 44,99 Swimming 40,55 Water polo 44,20 Gymnastics 40,51 Handball 43,50 Water polo 40,51 Swimming 43,20 Handball 40,12 Rowing 41,29 Volleyball 38,69 Volleyball 39,73 Rowing 38,19 Basketball 39,15 Basketball 37,43 Athletics - throwing 38,61 Athletics - throwing 35,69 Total average: 46,25 Total average: 42,33

Table 3. Average output results for 106 examinees, female and male separately.

Sport Average result (%) Athletics – long dist. running 55,59 Athletics – sprint/jump 52,02 Martial arts – kicking 51,78 Football 47,42 Tennis 44,53 Martial arts – push/pull 42,75 Water polo 42,31 Gymnastics 42,14 Swimming 41,95 Handball 41,68 Rowing 39,75 Volleyball 39,32 Basketball 38,42 Athletics - throwing 37,07 Total average: 44,05

In this chapter we have presented an expert system for the selection and identification of an optimal sport for a child. This is the first expert system developed for this purpose that uses fuzzy logic and has wide Internet accessibility. Expert knowledge stored in the knowledge

result (%) Sport Average

result (%)

Gender: Female, N = 46 Gender: Male, N = 61

Sport Average

N = 106, Min: 3,54 ; Max: 95,01 ; STD: 15,85

Table 4. Average output results for all examinees.

**6. Conclusion and discussion** 


**2** 

*Spain* 

**SeDeM Diagram: A New Expert System for the** 

The SeDeM expert system is a methodology which is applied in preformulation and formulation studies of medicines specifically in solid dosage forms. This system informs on the physical profile of powdered substances (APIs and excipients) used to formulate drugs (Suñé et al, 2005; García et al, 2010; Aguilar et al, 2009). By determining whether powders (API or excipient) are suitable for direct compression, the SeDeM profile will inform about the advantages and gaps of those powdered substance to be used in direct compression, so the system informs on whether the direct compression method is appropriate (e.g.. wet

The characterization of powdered substances by SeDeM facilitates the identification of the characteristics that require amendment in order to obtain tablets by direct compression. This system thus provides information that will ensure the robust design of the formulation in

This new method is based on the selection and application of several parameters that the formulation must fulfill to ensure a successful tablet elaborated by direct compression. The

a. The formulation must be representative and appropriate for the requirements of

b. The execution of the experimental methodology and calculus must be readily

SeDeM uses 12 tests (Suñé et al, 2005; García et al, 2010; Aguilar et al, 2009) to examine

Encarna García Montoya, Pilar Pérez Lozano, Johnny E. Aguilar Díaz, Manel Roig Carreras,

**1. Introduction** 

the final product.

applicable.

• Bulk density (Da) • Tapped density (Dc) • Inter-particle porosity (Ie)

• Carr index (IC)

 \*

following criteria are applied:

compression technology.

granulation should be applied before compression).

**2. Parameters examined by the SeDeM method** 

Roser Fuster García, Montserrat Miñarro Carmona, Josep R. Ticó Grau

whether a powder is suitable for direct compression.

*Service of Development of Medicines (SDM), Pharmaceutical Technology Unit, Pharmacy and Pharmaceutical Technology Department, University of Barcelona,* 

**Formulation of Drugs in Solid Form** 

Josep M. Suñé Negre, et al\*


## **SeDeM Diagram: A New Expert System for the Formulation of Drugs in Solid Form**

Josep M. Suñé Negre, et al\*

*Service of Development of Medicines (SDM), Pharmaceutical Technology Unit, Pharmacy and Pharmaceutical Technology Department, University of Barcelona, Spain* 

## **1. Introduction**

16 Expert Systems for Human, Materials and Automation

Dežman, B.; Trninić, S. & Dizdar, D. (2001a). Models of expert system and decision-making

Dežman, B, Trninić, S, Dizdar, D. (2001b). Expert model of decision-making system for

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Katić, R.; Miletić, Đ.; Maleš, B.; Grgantov, Z. & Krstulović, S. (2005). *Anthropological systems* 

Kim, W.; Song, Y. U. & Hong, J. S. (2005). Web enabled expert systems using hyperlink-

Leskošek, B.; Bohanec, M.; Rajkovič, V. & Šturm, J. (1992). Expert system for the assessment

MacDougall, J. D.; Wenger, H. A., & Green, H. J. (1991). *Physiological testing of the high-*

Morrow, J. & James, R. (2005). *Measurement and evaluation in human performance*. Human

Norton, K., & Olds, T. (2001). Morphological Evolution of Athletes Over the 20th Century –

Papić, V.; Rogulj, N. & Pleština, V. (2009). Identification of sport talents using a web-

Rogulj, N.; Papić, V.; & Pleština, V. (2006). Development of the Expert System for Sport

Rogulj, N.; Papić, V. & Čavala, M. (2009). Evaluation Models of Some Morphological

Shim, J. P.; Warkentin, M.; Courtney, J.F.; Power, D. J., Sharda, R. & Carlsson, C. (2002). Past,

Srhoj, Lj.; Mihaljević, D. & Čavala, M. (2010). Application of expert-system for talent

Stergiou, N. (2004). *Innovative Analysis of Human Movement*. Champaign, IL: Human Kinetics. Šimić, G. & Devedžić, V. (2003). Building an intelligent system using modern Internet

Rajković, V.; Bohanec, M.; Šturm, J.; Leskošek, B. (1991). An expert system for advising

Wagner, W. P.; Chung, Q. B. & Najdawi, M. K. (2003). The impact of problem domains and

technologies. *Expert Systems with Applications*, Issue 25, 231-246.

oriented expert system with a fuzzy module, *Expert Systems with Applications*. 36(5),

Talents Detection. *WSEAS Transactions on Information Science & Applications* , Issue

Characteristics for Talent Scouting in Sport, *Collegium Antropologicum*, 33(1), 105-

present, and future of decision support technology. *Decision Support Systems*,

children in choosing sports. *Proceedings of I. International Symposium "Sport of* 

knowledge acquisition techniques: a content analysis of P/OM expert system case

empirical verification. *Collegium antropologicum*, 25(1), 141-152.

mathematics, natural sciences and kinesiology, Split (in Croatian).

based inference. *Expert Systems with Applications*, Issue 28, 79-91.

and sport and the Zinman college of physical education, 45-52.


*performance athlete*. Champaign, IL: Human Kinetics.

Causes and Consequences. *Sports Med*, *31*(11), 763-783.

scouting in dancing. *Acta Kinesiologica* 4( 1), 109-113.

*Young"*, Faculty of Sport, Ljubljana, 641-646.

studies. *Expert Systems with Applications*, 24, 79–86.

players, *Kinesiology*, 33(2), 207-215.

Kinetics, Champaign.

3, Volume 9, 1752-1755.

Volume 33 (2), 111-126(16).

8830-8838.

110.

systems for efficient assessment of potential and actual quality of basketball

efficient orientation of basketball players to positions and roles in the game -

*in athletes: selection models and training models*, University of Split, Faculty of

 of sports talent in children. *Proceedings of the International conference of computer applications in sport and physical education*, Wingate institute for physical education

> The SeDeM expert system is a methodology which is applied in preformulation and formulation studies of medicines specifically in solid dosage forms. This system informs on the physical profile of powdered substances (APIs and excipients) used to formulate drugs (Suñé et al, 2005; García et al, 2010; Aguilar et al, 2009). By determining whether powders (API or excipient) are suitable for direct compression, the SeDeM profile will inform about the advantages and gaps of those powdered substance to be used in direct compression, so the system informs on whether the direct compression method is appropriate (e.g.. wet granulation should be applied before compression).

> The characterization of powdered substances by SeDeM facilitates the identification of the characteristics that require amendment in order to obtain tablets by direct compression. This system thus provides information that will ensure the robust design of the formulation in the final product.

> This new method is based on the selection and application of several parameters that the formulation must fulfill to ensure a successful tablet elaborated by direct compression. The following criteria are applied:


## **2. Parameters examined by the SeDeM method**

SeDeM uses 12 tests (Suñé et al, 2005; García et al, 2010; Aguilar et al, 2009) to examine whether a powder is suitable for direct compression.


<sup>\*</sup> Encarna García Montoya, Pilar Pérez Lozano, Johnny E. Aguilar Díaz, Manel Roig Carreras, Roser Fuster García, Montserrat Miñarro Carmona, Josep R. Ticó Grau

SeDeM Diagram: A New Expert System for the Formulation of Drugs in Solid Form 19

• Inter-particle porosity (Ie) of the powder mixture (Font, 1962) is calculated from the

• Carr index (IC%) (Córdoba et al, 1996; Rubinstein, 1993; Torres & Camacho, 1991; Wong, 1990). The method is described in Section 2.9.34 of Eur. Ph. (Ph Eur, 2011) This is

• Cohesion index (Icd): This index is determined by compressing the powder, preferably in an eccentric press. The mean hardness (N) of the tablets is calculated. First, the raw powder is tested, but if it cannot be compressed, 3.5% of the following mixture is added

• Hausner ratio (IH) (Ph Eur, 2011; Rubinstein, 1993). The method is described in Section 2.9.34 of Eur Ph (Ph Eur, 2011). This is calculated from Da and Dc as: IH=Dc/Da • Angle of repose (α) (Rubinstein, 1993, Muñoz, 1993). The method is described in Section 2.9.36 of Eur Ph (Ph Eur, 2011). This is the angle of the cone formed when the product is passed through a funnel with the following dimensions: height 9.5 cm, upper diameter of spout 7.2 cm, internal diameter at the bottom, narrow end of spout 1.8 cm. The funnel is placed on a support 20 cm above the table surface, centred over a millimetre-grid sheet on which two intersecting lines are drawn, crossing at the centre. The spout is plugged and the funnel is filled with the product until it is flush with the top end of the spout when smoothed with a spatula. Remove the plug and allow the powder to fall onto the millimetre sheet. Measure the four radii of the cone base with a slide calliper and calculate the mean value (r). Measure the cone height (h). Deduce α from

• Flowability (t″): The method is described in Section 2.9.16 of Eur. Ph (Ph Eur, 2011). It is expressed in seconds and tenths of a second per 100 grams of sample, with a mean

• Loss on drying (%HR): This is measured by the method described in 2.2.32 in Eur. Ph (Ph Eur, 2011). The sample is dried in an oven at 105 °C ± 2 °C, until a constant weight

• Hygroscopicity (%H): Determination of the percentage increase in sample weight after being kept in a humidifier at a relative humidity of 76% (±2%) and a temperature of

• Percentage of particles measuring <50 μm (%Pf): Particle size is determined by means of the sieve test following the General method 2.9.12 of Eur. Ph. (Ph Eur, 2011). The value returned is the % of particles that pass through a 0.05-mm sieve when vibrated for

• Homogeneity index (Iθ): This is calculated according to the General method 2.9.12 of Eur. Ph (Ph Eur, 2011).To determine particle size by means of the sieve test, the grain size of a 100g sample is measured by subjecting a sieve stack to vibration for 10 min at speed 10 (CISA vibrator). The sieve sizes used are 0.355 mm, 0.212 mm, 0.100 mm and 0.05 mm. The percentage of product retained in each sieve is calculated and the amount that passes through the 0.05mm sieve is measured. The percentage of fine particles (<50 μm) (%Pf) was calculated as described above. Note that if this percentage is higher than that calculated in the complete sieve test, it is because some of the particles become

to the mix: talc 2.36%, Aerosil® 200 0.14% and magnesium stearate 1.00%.

• Bulk density (Da): The method is described in Section 2.9.34 of Eur. Ph. (Ph Eur, 2011) • Tapped density (Dc): The method is described in Section 2.9.34 of Eur. Ph. (Ph Eur, 2011) The volume taken is the value obtained after 2500 strokes using a settling apparatus

with a graduated cylinder (voluminometer).

calculated from Da and Dc as: IC=(Dc-Da/Dc)100

following equation: Ie=Dc-Da/Dc×Da

tan(α)=h/r.

is obtained.

22°C± 2°C for 24 h.

value of three measurements.

10 min at speed 10 (CISA vibrator).


These tests are grouped into five factors on the basis of the physical characteristics of the powder and the functionality of the drug:

**Dimensional Parameter**. Bulk density (Da) and Tapped density (Dc). These affect the size of the tablet and its capacity to pile up. In addition, these tests are used in the calculus of other mathematical indexes for the determination of the compression parameter.

**Compressibility Parameter**. Inter-particle porosity (Ie), Carr index (IC) and Cohesion index (Icd). These affect the compressibility of the powder.

**Flowability/Powder Flow Parameter**. Hausner ratio (IH), Angle of repose (α) and Flowability (t″). These influence the flowability of the powdered substance when compressed.

**Lubricity/Stability Parameter**. Loss on drying (%HR) and Hygroscopicity (%H). These affect the lubricity and future stability of the tablets.

**Lubricity/Dosage parameter**. % Particles < 50 μm and Homogeneity Index. These influence the lubricity and dosage of the tablets.

Table 1 shows the 5 parameters, with the abbreviations, units, formulas and incidence on compression.


Table 1. Parameters and tests used by the SeDeM method.

## **2.1 Experimental procedure used to study a powdered substance with parameters considered by the SeDeM method**

Pharmacopoeia methodologies are used to calculate these parameters. When this is impossible, a common strategy used in pharmaceutical technology development is applied. The methods used for each test are described below (Pérez et al, 2006):

These tests are grouped into five factors on the basis of the physical characteristics of the

**Dimensional Parameter**. Bulk density (Da) and Tapped density (Dc). These affect the size of the tablet and its capacity to pile up. In addition, these tests are used in the calculus of other

**Compressibility Parameter**. Inter-particle porosity (Ie), Carr index (IC) and Cohesion index

**Flowability/Powder Flow Parameter**. Hausner ratio (IH), Angle of repose (α) and Flowability (t″). These influence the flowability of the powdered substance when

**Lubricity/Stability Parameter**. Loss on drying (%HR) and Hygroscopicity (%H). These

**Lubricity/Dosage parameter**. % Particles < 50 μm and Homogeneity Index. These influence

Table 1 shows the 5 parameters, with the abbreviations, units, formulas and incidence on

Tapped Density Dc g/ml Dc = P/Vc

Cohesion Index Icd N Experimental Hausner Ratio IH – IH = Dc/Da Angle of Repose (α) ° tg α = h/r

Powder Flow t″ s Experimental

Hygroscopicity %H % Experimental

Carr Index IC % IC = (Dc − Da/Dc) 100

Homogeneity Index (Iθ) – \* Iθ = Fm / 100 + Δ Fmn

Ie – Ie = Dc − Da/Dc × Da

mathematical indexes for the determination of the compression parameter.

Incidence factor Parameter Symbol Unit Equation Dimension Bulk Density Da g/ml Da = P/Va

Lubricity/Stability Loss on Drying %HR % Experimental

Lubricity/Dosage Particles < 50 μm %Pf % Experimental

**2.1 Experimental procedure used to study a powdered substance with parameters** 

Pharmacopoeia methodologies are used to calculate these parameters. When this is impossible, a common strategy used in pharmaceutical technology development is applied.

Inter-particle Porosity

Table 1. Parameters and tests used by the SeDeM method.

The methods used for each test are described below (Pérez et al, 2006):

**considered by the SeDeM method** 

• Cohesion index (Icd) • Hausner ratio (IH) • Angle of repose (α) • Flowability (t″) • Loss on drying (%HR) • Hygroscopicity (%H) • Particle size (%Pf) • Homogeneity index (Iθ)

compressed.

compression.

Compressibility

Flow

Flowability/Powder

powder and the functionality of the drug:

(Icd). These affect the compressibility of the powder.

affect the lubricity and future stability of the tablets.

the lubricity and dosage of the tablets.


SeDeM Diagram: A New Expert System for the Formulation of Drugs in Solid Form 21

• Icd. The limit is determined empirically from compression tests on many powdered substances, based on the maximum hardness obtained without producing capped or broken tablets. This hardness is then established as the maximum limit. The minimum value is "0". This value implies that no tablets are obtained when the powders are

• IH, Powder flow, repose angle. The limits are set on the basis of the monographs described in "Handbook of Pharmaceutical Excipients" (Kibbe, 2006), and monograph 2.9.36 of Ph Eur (Ph Eur, 2011) or other references in "Tecnologia Farmaceutica" by S.

• %HR. The limits are established on the basis of the references cited elsewhere, such as "Farmacotecnia teórica y práctica" by José Helman (Helman, 1981). The optimum

• Hygroscopicity is based on the "Handbook of Pharmaceutical Excipients" (Kibbe, 2006):

• Particle size. The limits are based on the literature. These sources (Kibbe, 2006) report that rheological and compression problems occur when the percentage of fine particles

The limits for the Homogeneity Index (Iθ) are based on the distribution of the particles of the powder (see Table 3, indicating the size of the sieve (in mm), average particle size in each fraction and the difference in average particle size in the fraction between 0.100 and 0.212 and the others). A value of 5 on a scale from 0 to 10 was defined as the minimum acceptable

> Average of the diameter of the

The major fraction (Fm) corresponds to the interval from 0.100 to 0.212 mm, because it falls in the middle of the other fractions of the table. This interval is calculated as the proportion in which the powder particles are found in each fraction considered in the table (as described above). Those particles located in the major fraction (Fm) in a proportion of 60% are considered to represent the MAV of 5. The distributions of the other particles are considered to be Gaussian. The limits for the Homogeneity Index are set between 0 and 0.02.

**2.3 Conversion of the limits considered in each parameter of the SeDeM method into** 

The numerical values of the parameters of the powder, which are obtained experimentally (v) as described above, are placed on a scale from 0 to 10, considering 5 as the MAV.

Corresponding diameter (dm ... dm ± n)

Dif dm with the mayor component

fraction

0,355 – 0,500 Fm+2 427 dm+2 271 0,212 – 0,355 Fm+1 283 dm+1 127 0,100 – 0,212 Fm 156 dm 0 0,050 –0,100 Fm-1 75 dm-1 81 < 0,050 Fm-2 25 dm-2 131

based on manitol (not hygroscopic) and sorbitol (highly hygroscopic).

(Casadio, 1972) and on monograph 2.9.36 of Ph Eur (Ph Eur, 2011).

compressed.

Casadio (Casadio, 1972).

humidity is between 1% to 3%.

in the formulation exceeds 25%.

Corresponding fraction

Table 3. Distribution of particles in the determination of Iθ.

**the radius (r) of the SeDeM Diagram** 

value (MAV), as follows:

Sieve (mm)

Carr Index, limits are based on references in "Tecnologia Farmaceutica" by S. Casadio

adhered to the product retained in the sieves during the grain-size test, and the percentage of <50 μm particles found may be lower than the true figure. The following equation is then applied to the data obtained.

( )( ) ( )( ) ( )( ) Fm \*I 100 dm dm-1 Fm-1 dm+1 dm Fm+1 dm dm-2 Fm-2 dm+2 dm Fm+2 .... dm dm-n Fm-n dm+n dm Fm+n θ = +− + − +− + − + +− + − (1)

Where:


## **2.2 Determination of acceptable limit values for each parameter included by the SeDeM method**

Having obtained the values as described above, certain limits are set (Table 2) on the basis of the parameters chosen and the values described in the Handbook of Pharmaceutical Excipients (Kibbe, 2006), or alternatively on the basis of experimental tests.


Table 2. Limit values accepted for the SeDeM Diagram parameters.

The rationale to establish the limits for each parameter is:

• Da, Dc, Ie e IC are calculated from the extreme values (excluding the most extreme values) described in "Handbook of Pharmaceutical Excipients" (Kibbe, 2006). For the

( )( ) ( )( )

100 dm dm-1 Fm-1 dm+1 dm Fm+1 dm dm-2 Fm-2 dm+2 dm Fm+2 ....

• Iθ, Relative homogeneity index. Particle-size homogeneity in the range of the fractions

• dm−1, mean diameter of the particles in the fraction of the range immediately below the

• dm+1, mean diameter of the particles in the fraction of the range immediately above the

Having obtained the values as described above, certain limits are set (Table 2) on the basis of the parameters chosen and the values described in the Handbook of Pharmaceutical

> Tapped density 0-1 g/ml Inter-particle porosity 0–1.2 Carr index 0–50 (%)

Cohesion index 0–200 (N) Hausner ratio 3–1 Angle of repose 50–0 (°)

Powder flow 20–0 (s)

Higroscopicity 20–0 (%)

Homogeneity index 0–2 × 10−2

**2.2 Determination of acceptable limit values for each parameter included by the** 

Incidence Parameter Acceptable range

Excipients (Kibbe, 2006), or alternatively on the basis of experimental tests.

Dimension Bulk density 0–1 g/ml

Lubricity/stability Loss on drying 0-10 (%)

Lubricity/dosage Particles < 50 μ 50–0 (%)

• Da, Dc, Ie e IC are calculated from the extreme values (excluding the most extreme values) described in "Handbook of Pharmaceutical Excipients" (Kibbe, 2006). For the

Table 2. Limit values accepted for the SeDeM Diagram parameters.

The rationale to establish the limits for each parameter is:

• Fm−1, percentage of particles in the range immediately below the majority range; • Fm+1, percentage of particles in the range immediately above the majority range; • n, order number of the fraction studied under a series, with respect to the major fraction;

+− + − +− + − +

(1)

equation is then applied to the data obtained.

( )( )

+− + −

θ =

Where:

studied;

majority range;

majority range.

**SeDeM method** 

Compressibility

Flowability/powder flow

Fm \*I

dm dm-n Fm-n dm+n dm Fm+n

• Fm, percentage of particles in the majority range;

• dm, mean diameter of the particles in the major fraction;

adhered to the product retained in the sieves during the grain-size test, and the percentage of <50 μm particles found may be lower than the true figure. The following Carr Index, limits are based on references in "Tecnologia Farmaceutica" by S. Casadio (Casadio, 1972) and on monograph 2.9.36 of Ph Eur (Ph Eur, 2011).


The limits for the Homogeneity Index (Iθ) are based on the distribution of the particles of the powder (see Table 3, indicating the size of the sieve (in mm), average particle size in each fraction and the difference in average particle size in the fraction between 0.100 and 0.212 and the others). A value of 5 on a scale from 0 to 10 was defined as the minimum acceptable value (MAV), as follows:


Table 3. Distribution of particles in the determination of Iθ.

The major fraction (Fm) corresponds to the interval from 0.100 to 0.212 mm, because it falls in the middle of the other fractions of the table. This interval is calculated as the proportion in which the powder particles are found in each fraction considered in the table (as described above). Those particles located in the major fraction (Fm) in a proportion of 60% are considered to represent the MAV of 5. The distributions of the other particles are considered to be Gaussian. The limits for the Homogeneity Index are set between 0 and 0.02.

## **2.3 Conversion of the limits considered in each parameter of the SeDeM method into the radius (r) of the SeDeM Diagram**

The numerical values of the parameters of the powder, which are obtained experimentally (v) as described above, are placed on a scale from 0 to 10, considering 5 as the MAV.

SeDeM Diagram: A New Expert System for the Formulation of Drugs in Solid Form 23

To determine whether the product is suitable for direct compression using a numerical method, the following indexes are calculated based on the SeDeM Diagram as follows:

<sup>≥</sup> <sup>−</sup>

No. p ≥ 5: Indicates the number of parameters whose value is equal to or higher than 5

*n P IP*

º 5 0,5 <sup>º</sup>

− = Parameter profile Index IPP Average of( )r all parameters (4)

<sup>−</sup> Good Compressibility Index IGC=IPP x f (5)

*n Pt*

Polygon area f Reliability factor Circle area = =

The reliability factor indicates that the inclusion of more parameters increases the reliability

n Pt

<sup>≥</sup> = = (3)

<sup>D</sup> (2)

(6)

D

Fig. 1. The SeDeM Diagram with 12 parameters.

nP 5 Parameter index IP=

No. Pt: Indicates the total number of parameters studied

Average (r) = mean value of the parameters calculated.

The acceptability limit would correspond to: IPP = media (r) = 5

The acceptability limit would correspond to: ICG = IPP x f = 5.

The acceptability limit would correspond to:

of the method (Figure 2).

**2.5 Acceptable limits for Indexes** 

Where:


Table 4. Conversion of limits for each parameter into radius values (r).

(a) The values that exceptionally appear below 1 are considered values corresponding to non-sliding products.

(b) Initially, relative humidity was calculated based on the establishment of three intervals because the percentage relation obtained from the measurement of the humidity of the substance does not follow a linear relation with respect to the correct behaviour of the dust. Humidity below 1% makes the powder too dry, and electrostatic charge is induced, which affects the rheology. Furthermore, low humidity percentages do not allow compression of the substance (moisture is necessary for compacting powders). Moreover, more than 3% moisture causes caking, in addition to favouring the adhesion to punches and dyes. Consequently, it was considered that this parameter should present optimal experimental values from 1% to 3% (Braidotti, 1974). Nevertheless, experience using the SeDeM Diagram has demonstrated no significant variations in the results, so the previous three intervals of relative humidity can be simplified to the calculation of the parameter, thus finally the linear criterion of treatment of results is adopted (Suñé et al, 2011).

The correspondence of the value of the parameters with this scale takes into account the limit values (see 2.2), using the factors indicated in Table 4. When all radius values are 10, the SeDeM Diagram takes the form of a circumscribed regular polygon, drawn by connecting all the radius values of the parameters with linear segments. Table 4 shows the factors used for calculating the numerical value of each parameter required for the SeDeM method.

## **2.4 Graphical representation of the SeDeM Diagram**

When all radius values are 10, the SeDeM Diagram takes the form of a circumscribed regular polygon, drawn by connecting the radius values with linear segments. The results obtained from the earlier parameter calculations and conversions are represented by the radius. The figure formed indicates the characteristics of the product and of each parameter that determines whether the product is suitable for direct compression. In this case, the SeDeM Diagram is made up of 12 parameters, thus forming an irregular 12-sided polygon (Figure 1).

Fig. 1. The SeDeM Diagram with 12 parameters.

## **2.5 Acceptable limits for Indexes**

To determine whether the product is suitable for direct compression using a numerical method, the following indexes are calculated based on the SeDeM Diagram as follows:

$$- \text{Parameter index} \quad \text{IP} = \frac{\text{n}^{\circ}\text{P} \ge 5}{\text{n}^{\circ}\text{Pt}} \tag{2}$$

Where:

22 Expert Systems for Human, Materials and Automation

Dimensions Bulk density 0–1 0–10 10v

Lubricity/estability Loss on drying (b) 10-0 0-10 10-v

Table 4. Conversion of limits for each parameter into radius values (r).

Lubricity/dosage Particles < 50 μ 50–0 0–10 10 − (v/5)

(a) The values that exceptionally appear below 1 are considered values corresponding to

(b) Initially, relative humidity was calculated based on the establishment of three intervals because the percentage relation obtained from the measurement of the humidity of the substance does not follow a linear relation with respect to the correct behaviour of the dust. Humidity below 1% makes the powder too dry, and electrostatic charge is induced, which affects the rheology. Furthermore, low humidity percentages do not allow compression of the substance (moisture is necessary for compacting powders). Moreover, more than 3% moisture causes caking, in addition to favouring the adhesion to punches and dyes. Consequently, it was considered that this parameter should present optimal experimental values from 1% to 3% (Braidotti, 1974). Nevertheless, experience using the SeDeM Diagram has demonstrated no significant variations in the results, so the previous three intervals of relative humidity can be simplified to the calculation of the parameter, thus finally the linear criterion of treatment of

The correspondence of the value of the parameters with this scale takes into account the limit values (see 2.2), using the factors indicated in Table 4. When all radius values are 10, the SeDeM Diagram takes the form of a circumscribed regular polygon, drawn by connecting all the radius values of the parameters with linear segments. Table 4 shows the factors used for

When all radius values are 10, the SeDeM Diagram takes the form of a circumscribed regular polygon, drawn by connecting the radius values with linear segments. The results obtained from the earlier parameter calculations and conversions are represented by the radius. The figure formed indicates the characteristics of the product and of each parameter that determines whether the product is suitable for direct compression. In this case, the SeDeM Diagram is made up of 12 parameters, thus forming an irregular 12-sided polygon (Figure 1).

calculating the numerical value of each parameter required for the SeDeM method.

**2.4 Graphical representation of the SeDeM Diagram** 

(v)

Tapped density 0–1 0–10 10v Inter-particle porosity 0–1.2 0–10 10v/1.2 Carr index 0–50 0–10 v/5

Cohesion index 0–200 0–10 v/20 Hausner ratio (a) 3–1 0–10 (30-10v)/2 Angle of repose 50–0 0–10 10 − (v/5)

Powder flow 20–0 0–10 10 − (v/2)

Higroscopicity 20–0 0–10 10 − (v/2)

Homogeneity index 0–2 × 10−2 0–10 500v

Radius (r)

Factor applied to v

Incidence Parameter Limit value

Compressibility

flow

Flowability/powder

non-sliding products.

results is adopted (Suñé et al, 2011).

No. p ≥ 5: Indicates the number of parameters whose value is equal to or higher than 5 No. Pt: Indicates the total number of parameters studied The acceptability limit would correspond to:

$$IP = \frac{n^{\circ}P \ge 5}{n^{\circ}Pt} = 0,5\tag{3}$$

$$- \text{Parameter profile Index} \quad \text{IPP} = \text{Average of (r) all parameters} \tag{4}$$

Average (r) = mean value of the parameters calculated. The acceptability limit would correspond to: IPP = media (r) = 5

$$- \text{Good Complexity Index} \quad \text{IGC=IPP x f} \tag{5}$$

$$\text{rf} = \begin{array}{c} \text{Reliability factor} = \frac{\text{Polygon area}}{\text{Circle area}} \end{array} \tag{6}$$

The acceptability limit would correspond to: ICG = IPP x f = 5.

The reliability factor indicates that the inclusion of more parameters increases the reliability of the method (Figure 2).

SeDeM Diagram: A New Expert System for the Formulation of Drugs in Solid Form 25

Tapped Density Dc g/ml 0.583 5.83

Inter-particle Porosity Ie – 0.517 4.31 Carr Index IC % 23.156 4.63

Cohesion Index Icd N 118.00 5.90

Hausner Ratio IH – 1.868 5.66 Angle of Repose (α) ° 25.770 4.85

Powder Flow t s 1.500 9.25

Hygroscopicity %H % 15.210 2.40

Homogeneity Index (Iθ) – 0.0058 2.90

(v)

(r) Mean

5.16

4.95

6.59

3.37

6.45

incidence

Incidence factor Parameter Symbol Unit Value

Compressibility

Flow

Flowability/Powder

calculation of radii.

direct compression.

Table 6. SeDeM acceptance index for API SX-325

type of incidence in this compression.

with controlled relative humidity below 25%.

Dimension Bulk Density Da g/ml 0.448 4.48

Lubricity/Stability Loss on Drying %HR % 5.650 4.35

Lubricity/Dosage Particles < 50 μm %Pf % 0.000 10.0

Table 5. Application of the SeDeM method to API in powder form (API SX-325), and

Parameter index 0.42 Parametric profile index (mean r of all parameters) 5.38 Good compression index (IGC) 5.12

On the basis of the results of the radius corresponding to the SeDeM Diagram, the parametric profile was > 5. This value implies that API SX-325 is suitable for direct compression. However, in order to discern the appropriateness of this substance for this formulation technology, we analyzed the 5 groups of individual factors classified by the

In the case study above, only the parameters involved in the general factor of denominated incidence lubrication/stability presented values below 5 (median = 3.37). This finding implies deficient rheological qualities and poor stability, expressed by a high intrinsic humidity of balance and high hygroscopicity. The product tended to capture humidity, thus worsening the rheological profile (compression, lack of flow) and consequently impairing its stability. These deficiencies are reflected graphically in the SeDeM Diagram, which shows that a large shaded area (activity area) (the greater the shaded area, the more suitable the characteristics for direct compression) is present for most of the parameters. However, some parameters show a small shaded area, thus indicating that the powder is not suitable for

In this regard, the SeDeM method informed (table 5) on the following for API SX-325: it is a dusty substance with correct dimensional characteristics (Da and Dc); it shows moderately acceptable compressibility (IE, IC, Icd), which can be improved with the addition of excipients of direct compression (DC); it shows very good fluidity/flowability (IH, α, t") and correct lubrication/dosage (%Pf, Iθ). Given these characteristics API SX-325 is suitable for compression with the addition of standardized formula of lubricant. The group of factors with deficient incidence corresponds to lubricity/stability and, considering the parameters HR and H, corrective measures can be taken to prevent its negative influence on direct compression. These measures include drying the material and preparing the tablet in rooms

Fig. 2. On the left graph with ∞ parameters (maximum reliability), f = 1. In the center, graph with 12 parameters (nº of parameters in this study), f = 0.952. On the right, graph with 8 parameters (minimum reliability), f = 0.900.

## **3. Practical applications of SeDeM**

#### **3.1 Determination of the suitability of an API to be subjected to direct compression technology**

Here we used the SeDeM method to characterize an active product ingredient in powder form (API SX-325) and to determine whether it is suitable for direct compression, applying the profile to the SeDeM Diagram.

We measured the 12 parameters proposed in the SeDeM method following the procedures indicated. Thus we obtained the values on which the factors set out in Table 5 are applied to obtain the numerical values corresponding to the radius of the diagram and the values of the mean incidence. All the values in Table 5 correspond to the average of two determinations. The radius values are represented in the diagram shown in Figure 3.

Fig. 3. SeDeM Diagram for API SX-325.

To obtain the indices of acceptance or qualification for formulation by direct compression, the formulas corresponding to the parametric index were applied from the numerical results of the radius shown in Table 5. The results of the acceptance indices are shown in Table 6.

2

6

3

5

4

**0**

6

8

7

5

**5**

2

4

3

**10** 1

**0**

8

12

9

10

parameters (minimum reliability), f = 0.900.

**3. Practical applications of SeDeM** 

the profile to the SeDeM Diagram.

**technology** 

11

7

Fig. 2. On the left graph with ∞ parameters (maximum reliability), f = 1. In the center, graph with 12 parameters (nº of parameters in this study), f = 0.952. On the right, graph with 8

**3.1 Determination of the suitability of an API to be subjected to direct compression** 

Here we used the SeDeM method to characterize an active product ingredient in powder form (API SX-325) and to determine whether it is suitable for direct compression, applying

We measured the 12 parameters proposed in the SeDeM method following the procedures indicated. Thus we obtained the values on which the factors set out in Table 5 are applied to obtain the numerical values corresponding to the radius of the diagram and the values of the mean incidence. All the values in Table 5 correspond to the average of two

determinations. The radius values are represented in the diagram shown in Figure 3.

0

(α )

To obtain the indices of acceptance or qualification for formulation by direct compression, the formulas corresponding to the parametric index were applied from the numerical results of the radius shown in Table 5. The results of the acceptance indices are shown in Table 6.

t

(Iθ )

%HR

Fig. 3. SeDeM Diagram for API SX-325.

%H

% Pf

5

10 Da

Dc

IH

Ie

IC

Icd

**5**

**10** 1


Table 5. Application of the SeDeM method to API in powder form (API SX-325), and calculation of radii.


Table 6. SeDeM acceptance index for API SX-325

On the basis of the results of the radius corresponding to the SeDeM Diagram, the parametric profile was > 5. This value implies that API SX-325 is suitable for direct compression. However, in order to discern the appropriateness of this substance for this formulation technology, we analyzed the 5 groups of individual factors classified by the type of incidence in this compression.

In the case study above, only the parameters involved in the general factor of denominated incidence lubrication/stability presented values below 5 (median = 3.37). This finding implies deficient rheological qualities and poor stability, expressed by a high intrinsic humidity of balance and high hygroscopicity. The product tended to capture humidity, thus worsening the rheological profile (compression, lack of flow) and consequently impairing its stability. These deficiencies are reflected graphically in the SeDeM Diagram, which shows that a large shaded area (activity area) (the greater the shaded area, the more suitable the characteristics for direct compression) is present for most of the parameters. However, some parameters show a small shaded area, thus indicating that the powder is not suitable for direct compression.

In this regard, the SeDeM method informed (table 5) on the following for API SX-325: it is a dusty substance with correct dimensional characteristics (Da and Dc); it shows moderately acceptable compressibility (IE, IC, Icd), which can be improved with the addition of excipients of direct compression (DC); it shows very good fluidity/flowability (IH, α, t") and correct lubrication/dosage (%Pf, Iθ). Given these characteristics API SX-325 is suitable for compression with the addition of standardized formula of lubricant. The group of factors with deficient incidence corresponds to lubricity/stability and, considering the parameters HR and H, corrective measures can be taken to prevent its negative influence on direct compression. These measures include drying the material and preparing the tablet in rooms with controlled relative humidity below 25%.

SeDeM Diagram: A New Expert System for the Formulation of Drugs in Solid Form 27

The unknown values are replaced by the calculated ones required for each substance in order to obtain R = 5 (5 is the minimum value considered necessary to achieve satisfactory compression). For example, if a deficient compressibility parameter for an API requires correction, Equation 7 is applied by replacing the terms RE and RP with the values calculated for each substance with the purpose to obtain a R=5, thus obtaining the optimal excipient to design a first drug formulation and the maximum amount required for a comprehensive understanding of the proposed formula. From this first formulation, research can get underway for the final optimization of the formulation, taking into consideration the biopharmaceutical characteristics required in the final tablet (disintegration, dissolution, etc). We thus present a method to establish the details of the

**3.2.1 Practical application of the mathematical equation to calculate the amount of excipient required for a deficient API to be subjected to direct compression** 

When an API requires an appropriate formula for the direct compression, it must be characterized following the SeDeM Diagram. Furthermore, a series of excipients used for DC are also characterized using the diagram. If the API has deficient compressibility parameters (<5), it is mixed with an excipient with a satisfactory compressibility parameter (>5), thereby correcting the deficiency. The excipient that shows the smallest amount to correct this parameter should be used. The amount of excipient is determined by the

Here we describe an example using an API 842SD and 6 diluents used for DC. The corresponding parameters and the radius mean values obtained with samples of this substance are shown in Table 7 and the parameters and the radius mean values of six

Incidence factor Parameter Symbol Unit Value (v) (r) Mean

Tapped Density Dc g/ml 1.140 10.00

Inter-particle Porosity Ie – 0.413 3.44 Carr Index IC % 32.018 6.40

Cohesion Index Icd N 7.330 0.37

Hausner Ratio IH – 1.98 5.10 Angle of Repose (α) ° 37.450 2.51

Powder Flow t s 10.330 4.84

Hygroscopicity %H % 0.007 10.0

Homogeneity Index (Iθ) 0.0024 1.20

Dimension Bulk Density Da g/ml 0.775 7.75

Lubricity/Stability Loss on Drying %HR % 9.865 0.68

Lubricity/Dosage Particles < 50 μm %Pf % 12.000 7.60

Parameter index 0.50 Parametric profile index (mean r of all parameters) 4.99 Good compression index (IGC) 4.75 Table 7. Parameters, mean incidence and parametric index for API 842SD incidence

8.88

3.40

4.15

5.34

4.40

RE = mean-incidence radius value (compressibility) of the corrective excipient

RP = mean-incidence radius value (compressibility) of the API to be corrected

R = mean-incidence radius value to be obtained in the blend

formulation of a given drug by direct compression.

mathematical equation of the SeDeM system (Equation 7).

excipient diluents used in DC are shown in Table 8 (Suñé et al, 2008a).

**technology** 

Compressibility

Flow

Flowability/Powder

The results given by the SeDeM method in this example demonstrate that it is reliable in establishing whether powdered substances have suitable profiles to be subjected to direct compression. Consequently, SeDeM is a tool that will contribute to preformulation studies of medicines and help to define the manufacturing technology required. Indeed, the application of the SeDeM Diagram allows the determination of the direct compression behaviour of a powdered substance from the index of parametric profile (IPP) and the index of good compression (IGC), in such a way that an IPP and an IGC equal or over 5 indicates that the powder displays characteristics that make it suitable for direct compression, adding only a small amount of lubricant (3.5% of the magnesium stearate, talc and Aerosil® 200). Also, with IPP and IGC values between 3 and 5, the substance will require a DC diluent excipient suitable for direct compression. In addition, it is deduced that techniques other than direct compression (wet granulation or dry granulation) will be required for APIs with IPP and IGC values below 3.

The SeDeM Diagram is not restricted to active products since it can also be used with new or known excipients to assess their suitability for application as adjuvants in direct compression. Thus, knowledge of excipient profiles, with their corresponding parameters, will allow identification of the most suitable excipient to correct the characteristics of APIs registering values under 5.

Of note, the greater the number of parameters selected, the greater the reliability of the method, in such a way that to obtain a reliability of the 100%, the number of parameters applied would have to be infinite (reliability factor = 1). The number of parameters could be extended using additional complementary ones, such as the true density, the index of porosity, the electrostatic charge, the specific surface, the adsorption power, % of lubrication, % friability, and the index of elasticity. However, while improving the reliability of the method, the inclusion of further parameters would be to the detriment of its simplicity and rapidity, since complementary parameters are difficult to apply.

#### **3.2 Application of the SeDeM method to determine the amount of excipient required for the compression of an API that is not apt for direct compression**

Experimental determination of the parameters of the SeDeM method for a range of APIs and excipients allows definition of their corresponding compressibility profiles and their subsequent mathematical treatment and graphical expression (SeDeM Diagram). Various excipient diluents can be analyzed to determine whether a substance is appropriate for direct compression and the optimal proportion of excipient required to design a suitable formulation for direct compression based on the SeDeM characteristics of the API (Suñé et al, 2008a). In this regard, the SeDeM method is a valid tool with which to design the formulation of tablets by direct compression.

The mathematical equation can be applied to the 5 parameters (dimension, compressibility, flowability/powder flow, lubricity/stability lubricity/dosage) considered deficient by the SeDeM system. The mathematical equation is applied to correct a deficient parameter of the API. The equation proposed (Equation 7) allows calculation of the amount of excipient required to compress the API on the basis of the SeDeM radius considered minimum (5) for each parameter of incidence that allows correct compression.

$$\text{CP} = 100 - \left(\frac{\text{RE} - \text{R}}{\text{RE} - \text{RP}} \times 100\right) \tag{7}$$

Where: CP = % of corrective excipient RE = mean-incidence radius value (compressibility) of the corrective excipient

R = mean-incidence radius value to be obtained in the blend

26 Expert Systems for Human, Materials and Automation

The results given by the SeDeM method in this example demonstrate that it is reliable in establishing whether powdered substances have suitable profiles to be subjected to direct compression. Consequently, SeDeM is a tool that will contribute to preformulation studies of medicines and help to define the manufacturing technology required. Indeed, the application of the SeDeM Diagram allows the determination of the direct compression behaviour of a powdered substance from the index of parametric profile (IPP) and the index of good compression (IGC), in such a way that an IPP and an IGC equal or over 5 indicates that the powder displays characteristics that make it suitable for direct compression, adding only a small amount of lubricant (3.5% of the magnesium stearate, talc and Aerosil® 200). Also, with IPP and IGC values between 3 and 5, the substance will require a DC diluent excipient suitable for direct compression. In addition, it is deduced that techniques other than direct compression (wet granulation or dry granulation) will be required for APIs with

The SeDeM Diagram is not restricted to active products since it can also be used with new or known excipients to assess their suitability for application as adjuvants in direct compression. Thus, knowledge of excipient profiles, with their corresponding parameters, will allow identification of the most suitable excipient to correct the characteristics of APIs

Of note, the greater the number of parameters selected, the greater the reliability of the method, in such a way that to obtain a reliability of the 100%, the number of parameters applied would have to be infinite (reliability factor = 1). The number of parameters could be extended using additional complementary ones, such as the true density, the index of porosity, the electrostatic charge, the specific surface, the adsorption power, % of lubrication, % friability, and the index of elasticity. However, while improving the reliability of the method, the inclusion of further parameters would be to the detriment of its

**3.2 Application of the SeDeM method to determine the amount of excipient required** 

Experimental determination of the parameters of the SeDeM method for a range of APIs and excipients allows definition of their corresponding compressibility profiles and their subsequent mathematical treatment and graphical expression (SeDeM Diagram). Various excipient diluents can be analyzed to determine whether a substance is appropriate for direct compression and the optimal proportion of excipient required to design a suitable formulation for direct compression based on the SeDeM characteristics of the API (Suñé et al, 2008a). In this regard, the SeDeM method is a valid tool with which to design the

The mathematical equation can be applied to the 5 parameters (dimension, compressibility, flowability/powder flow, lubricity/stability lubricity/dosage) considered deficient by the SeDeM system. The mathematical equation is applied to correct a deficient parameter of the API. The equation proposed (Equation 7) allows calculation of the amount of excipient required to compress the API on the basis of the SeDeM radius considered minimum (5) for

> RE R CP 100 <sup>100</sup> RE RP ⎛ ⎞ <sup>−</sup> =− × ⎜ ⎟

⎝ ⎠ <sup>−</sup> (7)

simplicity and rapidity, since complementary parameters are difficult to apply.

**for the compression of an API that is not apt for direct compression** 

formulation of tablets by direct compression.

Where:

CP = % of corrective excipient

each parameter of incidence that allows correct compression.

IPP and IGC values below 3.

registering values under 5.

RP = mean-incidence radius value (compressibility) of the API to be corrected

The unknown values are replaced by the calculated ones required for each substance in order to obtain R = 5 (5 is the minimum value considered necessary to achieve satisfactory compression). For example, if a deficient compressibility parameter for an API requires correction, Equation 7 is applied by replacing the terms RE and RP with the values calculated for each substance with the purpose to obtain a R=5, thus obtaining the optimal excipient to design a first drug formulation and the maximum amount required for a comprehensive understanding of the proposed formula. From this first formulation, research can get underway for the final optimization of the formulation, taking into consideration the biopharmaceutical characteristics required in the final tablet (disintegration, dissolution, etc). We thus present a method to establish the details of the formulation of a given drug by direct compression.

## **3.2.1 Practical application of the mathematical equation to calculate the amount of excipient required for a deficient API to be subjected to direct compression technology**

When an API requires an appropriate formula for the direct compression, it must be characterized following the SeDeM Diagram. Furthermore, a series of excipients used for DC are also characterized using the diagram. If the API has deficient compressibility parameters (<5), it is mixed with an excipient with a satisfactory compressibility parameter (>5), thereby correcting the deficiency. The excipient that shows the smallest amount to correct this parameter should be used. The amount of excipient is determined by the mathematical equation of the SeDeM system (Equation 7).

Here we describe an example using an API 842SD and 6 diluents used for DC. The corresponding parameters and the radius mean values obtained with samples of this substance are shown in Table 7 and the parameters and the radius mean values of six excipient diluents used in DC are shown in Table 8 (Suñé et al, 2008a).


Table 7. Parameters, mean incidence and parametric index for API 842SD


Table 8. Radius parameters, mean incidence and parametric index for excipients DC

SeDeM Diagram: A New Expert System for the Formulation of Drugs in Solid Form 29

Dc

IH

Ie

IC

Icd

Plasdone® S630

Prosolv® HD90

Isolmalt® 721

0

(α)

The SeDeM Diagram for API 842SD (Figure 4, Table 7) indicates that this substance has deficient compressibility (r=3.40), limited rheological characteristics (r=4.15) and low lubricity/dosage (r=4.40). Consequently, to apply direct compression to API 842SD, it requires formulation with an excipient that enhances the compressibility factor. This

In order to select the excipient and the concentration used to correct the deficiencies and, in particular, the compressibility, we applied the mathematical equation of the SeDeM Expert system (Equation 7): replacing the unknowns (RE and RP) with the values calculated for each substance (RE for excipients and RP for API) with aim to obtain R=5. The results

VA®

% excipient 44.32 41.03 45.33 29.09 72.07 59.04 Table 9. Amount of excipient required to be mixed with the API to obtain a compressibility

Plasdone S630 was the most suitable excipient to correct the deficit (compressibility) of API

To better understand the SeDeM system, the graphical representations of the profiles of the API and the excipient can be superposed. Figure 5 shows how the deficiencies of an API would be compensated when formulated. The green line corresponds to the excipient that theoretically provides the final mixture the characteristics to be compressed. In this way, the information provided by the SeDeM system allows the formulator to start working with excipients that have a high probability to provide suitable formulations, thus reducing the

7.01 7.30 6.93 8.90 5.62 6.11 3.40 3.40 3.40 3.40 3.40 3.40 5.00 5.00 5.00 5.00 5.00 5.00

t

Kleptose® Koll

(Iθ)

%HR

Fig. 4. SeDeM Diagram for API 842SD

excipient is identified by the SeDeM system.

obtained are shown in Table 9.

PH101

842SD with the lowest concentration (29.09 %). (Table 9)

EXCIPIENT Avicel®

RE RP (API) R

factor equal to 5.

lead time of formulation.

%H

% Pf

5

10 Da

Fig. 4. SeDeM Diagram for API 842SD

28 Expert Systems for Human, Materials and Automation

Table 8. Radius parameters, mean incidence and parametric index for excipients DC

Avicel

PH 101

Batch 6410C

Isomalt®

Batch LRE 539

Kleptose®

Batch 774639

Kollindon®

VA64

Batch 28-2921

Plasdone

®S630

Batch 6272473

Prosolv®

HD90

Batch K950044

PARAMETERS ( radius ) FACTOR INDEX Excipient Da Dc Ie IC Icd IH α t" %HR %H %pf (Iθ)

Dimension.

3.47 4.63 6.02 5.01 10.00 5.55 3.46 0.00 3.84 8.17 3.38 10.00 4.05 7.01 3.01 6.01 6.69 0.50 5.29 5.04

4.40 5.60 4.06 4.29 10.00 5.76 6.24 6.85 4.01 9.89 9.00 2.00 5.00 6.11 6.28 6.95 5.50 0.58 6.01 5.72

5.58 8.46 5.08 6.81 10.00 4.95 3.51 6.50 0.00 8.12 3.60 1.90 7.02 7.30 4.98 4.06 2.75 0.58 5.38 5.12

2.53 3.43 8.64 5.25 6.91 5.48 6.04 5.25 3.19 2.85 8.40 5.50 2.98 6.93 5.59 3.02 6.95 0.67 5.29 5.03

2.48 3.73 10.00 6.70 10.00 4.99 4.13 0.00 3.46 3.17 3.60 5.70 3.11 8.90 3.04 3.32 4.65 0.33 4.83 4.60

4.86 5.96 3.17 3.69 10.00 5.91 5.99 6.75 3.44 8.86 6.24 10.00 5.41 5.62 6.22 6.15 8.12 0.67 6.24 5.94

Compressibility

Flowability/

Powder Flow

Lubricity/

Stability.

Lubricity/

Dosage

IP PP IGC

The SeDeM Diagram for API 842SD (Figure 4, Table 7) indicates that this substance has deficient compressibility (r=3.40), limited rheological characteristics (r=4.15) and low lubricity/dosage (r=4.40). Consequently, to apply direct compression to API 842SD, it requires formulation with an excipient that enhances the compressibility factor. This excipient is identified by the SeDeM system.

In order to select the excipient and the concentration used to correct the deficiencies and, in particular, the compressibility, we applied the mathematical equation of the SeDeM Expert system (Equation 7): replacing the unknowns (RE and RP) with the values calculated for each substance (RE for excipients and RP for API) with aim to obtain R=5. The results obtained are shown in Table 9.


Table 9. Amount of excipient required to be mixed with the API to obtain a compressibility factor equal to 5.

Plasdone S630 was the most suitable excipient to correct the deficit (compressibility) of API 842SD with the lowest concentration (29.09 %). (Table 9)

To better understand the SeDeM system, the graphical representations of the profiles of the API and the excipient can be superposed. Figure 5 shows how the deficiencies of an API would be compensated when formulated. The green line corresponds to the excipient that theoretically provides the final mixture the characteristics to be compressed. In this way, the information provided by the SeDeM system allows the formulator to start working with excipients that have a high probability to provide suitable formulations, thus reducing the lead time of formulation.

SeDeM Diagram: A New Expert System for the Formulation of Drugs in Solid Form 31

pharmacotechnical equivalence between batches. Correct reproducibility between batches will ensure the reproducibility and the quality of the tablets formulated with this API or

Figure 6 shows the SeDeM Diagrams of three batches from the same API (Perez et al, 2006). In this case the mark and the indices were very similar. This control has the advantage that the method has the capacity to detect variations in particle size between batches of the product. This capacity thus contributes to the formulation of the pharmaceutical forms and

The SeDeM system also allows differentiation between excipients of the same chemical family but that differ in physical characteristics. These characteristics will determine their use in a formulation for direct compression of a given API. In a previous study (Suñé et al, 2008b) several lactoses were characterized, and in figure 7 can be observed the clear differentiation that makes the SeDeM methodology between the same chemical substances

0

(α )

Fig. 7. SeDeM Diagram for three kinds of lactose. On the left: Lactose anhydre IGC: 5.39. In the center: Lactose monohydrate IGC: 4.83. On the right: Lactose fast-flow IGC: 6.30.

Also, the SeDeM Expert system allows differentiation between excipients from the same functional type, for example disintegrants or diluents. In the former, the SeDeM characterization provides the information required to predict the difficulties encountered for

By quantifying the 12 tests provided by the system, the deficient values for their compression can be defined; on the basis of these values, an adequate (applying the same SeDeM Diagram) substance can be selected to improve the compressibility in the final mixture of the disintegrants and the API. Figure 8 shows the characterization of several disintegrants using the SeDeM technique, where the differences between each one in relation to their major or minor compression capacity are shown, although all are used

t

**3.5 Application of the SeDeM Diagram to differentiate excipients of the same** 

(Iθ )

5

10 Da

Dc

IH

Ie

IC

0

(α )

t

(Iθ )

5

10 Da

Dc

IH

Ie

IC

Icd

%HR

%H

% Pf

Icd

**3.4 Application of the SeDeM method to differentiate the excipient in the same** 

excipient, regardless of the batch used.

their correct dissolution.

(but different functionally).

0

(α )

t

**functional type** 

compression.

(Iθ )

%HR

%H

% Pf

5

10 Da

Dc

IH

Ie

IC

because of their disintegrant function (Aguilar et al, 2009).

%HR

%H

% Pf

Icd

**chemical family** 

Fig. 5. Green indicates the part that corresponds to the excipient that provides suitable compressibility to the final mixture with the API (in yellow). Three excipients are shown, all of them covering the deficiencies of the API.

#### **3.3 Application of the SeDeM system to the quality control of batches of a single API or excipient used for direct compression**

The SeDeM system is also apt for verification of the reproducibility of manufacturing standards between batches of the same powdered raw material (API or excipient). Indeed, superposing the SeDeM Diagrams of each batch, the degree of similarity or difference between the same API on the basis of the established parameters can determine its appropriateness for compression.

Fig. 6. SeDeM Diagram of 3 batches of API FO130.

The SeDeM method is also a useful tool for the study of the reproducibility of a manufacturing method used for a powdered substance and, thus of the validation of systematic variation during elaboration. A manufacturing process gives rise to variations in the final product and these variations must fall within limits or established specifications. By applying the SeDeM method to study reproducibility between batches of the same API or excipient, specifications in the different parameters can be established to ensure the same quality of the product regardless of the batch analyzed. In addition, these specifications must be used for the establishment of particular limits for quality control applications. To achieve this goal it is necessary to study the parameters of the SeDeM Diagram, applying the same statistic analyses required to establish the

Dc

IH

Ie

IC

0

(α)

**LOTE 40011**

0

(α )

t

(Iθ )

%HR

%H

% Pf

5

10 Da

Dc

IH

Ie

IC

Icd

t

(Iθ)

%HR

%H

% Pf

5

10 Da

Dc

IH

Ie

IC

Icd

Icd

0

(α)

**3.3 Application of the SeDeM system to the quality control of batches of a single API** 

The SeDeM system is also apt for verification of the reproducibility of manufacturing standards between batches of the same powdered raw material (API or excipient). Indeed, superposing the SeDeM Diagrams of each batch, the degree of similarity or difference between the same API on the basis of the established parameters can determine its

**LOTE 40009**

0

(α )

The SeDeM method is also a useful tool for the study of the reproducibility of a manufacturing method used for a powdered substance and, thus of the validation of systematic variation during elaboration. A manufacturing process gives rise to variations in the final product and these variations must fall within limits or established specifications. By applying the SeDeM method to study reproducibility between batches of the same API or excipient, specifications in the different parameters can be established to ensure the same quality of the product regardless of the batch analyzed. In addition, these specifications must be used for the establishment of particular limits for quality control applications. To achieve this goal it is necessary to study the parameters of the SeDeM Diagram, applying the same statistic analyses required to establish the

t

(Iθ )

%HR

%H

% Pf

5

10 Da

Dc

IH

Ie

IC

Icd

Fig. 5. Green indicates the part that corresponds to the excipient that provides suitable compressibility to the final mixture with the API (in yellow). Three excipients are shown, all

t

(Iθ)

5

10 Da

0

(α)

appropriateness for compression.

Dc

IH

Ie

IC

Icd

Fig. 6. SeDeM Diagram of 3 batches of API FO130.

**LOTE 40008**

0

(α )

t

(Iθ )

%HR

%H

% Pf

5

10 Da

t

(Iθ)

%HR

%H

% Pf

5

10 Da

Dc

IH

of them covering the deficiencies of the API.

**or excipient used for direct compression** 

Ie

IC

%HR

%H

% Pf

Icd

pharmacotechnical equivalence between batches. Correct reproducibility between batches will ensure the reproducibility and the quality of the tablets formulated with this API or excipient, regardless of the batch used.

Figure 6 shows the SeDeM Diagrams of three batches from the same API (Perez et al, 2006). In this case the mark and the indices were very similar. This control has the advantage that the method has the capacity to detect variations in particle size between batches of the product. This capacity thus contributes to the formulation of the pharmaceutical forms and their correct dissolution.

### **3.4 Application of the SeDeM method to differentiate the excipient in the same chemical family**

The SeDeM system also allows differentiation between excipients of the same chemical family but that differ in physical characteristics. These characteristics will determine their use in a formulation for direct compression of a given API. In a previous study (Suñé et al, 2008b) several lactoses were characterized, and in figure 7 can be observed the clear differentiation that makes the SeDeM methodology between the same chemical substances (but different functionally).

Fig. 7. SeDeM Diagram for three kinds of lactose. On the left: Lactose anhydre IGC: 5.39. In the center: Lactose monohydrate IGC: 4.83. On the right: Lactose fast-flow IGC: 6.30.

## **3.5 Application of the SeDeM Diagram to differentiate excipients of the same functional type**

Also, the SeDeM Expert system allows differentiation between excipients from the same functional type, for example disintegrants or diluents. In the former, the SeDeM characterization provides the information required to predict the difficulties encountered for compression.

By quantifying the 12 tests provided by the system, the deficient values for their compression can be defined; on the basis of these values, an adequate (applying the same SeDeM Diagram) substance can be selected to improve the compressibility in the final mixture of the disintegrants and the API. Figure 8 shows the characterization of several disintegrants using the SeDeM technique, where the differences between each one in relation to their major or minor compression capacity are shown, although all are used because of their disintegrant function (Aguilar et al, 2009).

SeDeM Diagram: A New Expert System for the Formulation of Drugs in Solid Form 33

Here we developed an original methodology for the preformulation and powder substance characterization. This method facilitates studies on the design and development of formulations for the production of tablets by direct compression. The SeDeM expert system is a useful tool because, in addition to considering the type of components, it also provides recommendations on intrinsic properties, such as the characteristics and morphology of the particles. We propose that given the accuracy of the information provided by this system,

This method characterizes the individual components of a formulation and applies a mathematical analysis to determine the exact amount of each in the final formulation. The formulation provided will be valid for direct compression. This manufacturing procedure offers many advantages from a production perspective. In addition to being faster than other techniques, it is straightforward as it reduces the number of steps during

In addition SeDeM has the advantage of providing formulation with the lowest amount of excipients as it combines the API with only one excipient and the standard formula of lubricants, thus avoiding the used of unnecessary excipients, such as diluents, binders and

The information given by the SeDeM system contributes to a Quality by Design Development. Consequently, this innovative tool is consistent with the current requirements

Aguilar\_Díaz, J.E.; García-Montoya, E.; Pérez-Lozano, P.; Suñé-Negre, J.M.; Miñarro, M. &

Braidotti, L. & Bulgarelli, D. (1974) *Tecnica Farmaceutica*. (1ª ed), Lleditrice Scientifica LG

Brittain, H.G. (1997). On the Physical Characterization of Pharmaceutical Solids. *Pharm* 

Córdoba Borrego, M.; Moreno Cerezo, J.M.; Córdoba Díaz, M. & Córdoba Díaz, D. (1996).

Font Quer, P. *Medicamenta: guía teórico práctica para farmacéuticos y médicos.* (1962) (6th ed),

García Montoya, E.; Suñé Negre, J.M.; Pérez Lozano, P.; Miñarro Carmona, M. & Ticó Grau,

directa con agentes hidrotrópicos. *Inf Farm*, 4, pp. 65-70, ISSN: 0213-5574 *European Pharmacopeia.* (2011) (7th ed), Council of Europe, ISBN: 978-92-871-6053-9,

Preformulación y desarrollo galénico de nuevas formulaciones por compresión

J.R. (2010). Metodología de preformulación galénica para la caracterización de

Casadio, S. (1972). *Tecnologia Farmaceutica.* (2ª ed), Cisalpino-Goliardica Ed., Milan

Ticó, J.R. (2009). The use of the SeDeM Diagram expert system to determine the suitability of diluents-disintegrants for direct compression and their use in formulation of ODT. *Eur J Pharm & Biopharm*, 73, pp. 414-423, ISSN: 0939-6411 Aguilar\_Díaz, J.E.; García\_Montoya, E.; Pérez\_Lozano, P.; Suñé\_Negre, J.M.; Miñarro, M. &

Ticó, J.R. (2011). Contribution to development of ODT using an innovator tool: SeDeM-ODT. *Proceedings of X Congreso de la Sociedad Española de Farmacia Industrial y* 

formulations will have a higher probability of being successfully compressed.

of regulatory health authorities such as the FDA and ICH.

*Galénica,* Madrid, 2-4 febrero 2011.

*Techn*, 1, pp. 100-106, ISSN: 1543-2521

Labor Ed., Barcelona (1): 340 - 341.

Guadagni, Milan

Strasbourgh

**4. Conclusions** 

the manufacturing process.

agglutinants.

**5. References** 

Fig. 8. SeDeM diagram for several disintegrant excipients.

## **3.6 The new model SeDeM-ODT to develop orally disintegrating tablets by direct compression**

This innovative tool is the new SeDeM-ODT model which provides the Index of Good Compressibility & Bucodispersibility (IGCB index) obtained from the previous SeDeM method (Aguilar et al, 2011). The IGCB index is composed by 6 factors that indicate whether a mixture of powder lends itself to be subjected to direct compression. Moreover, the index simultaneously indicates whether these tablets are suitable as bucodispersible tablet (disintegration in less than 3 minutes). The new factor, disgregability (Table 10), has three parameters that influence this parameter. The graph now comprises 15 parameters (Figure 9).


Table 10. The new factor disgregability is added to the SeDeM expert system to achieve the SeDeM-ODT expert system.

Fig. 9. SeDeM-ODT Diagram

## **4. Conclusions**

32 Expert Systems for Human, Materials and Automation

Fig. 8. SeDeM diagram for several disintegrant excipients.

**compression** 

Disgregability

SeDeM-ODT expert system.

Fig. 9. SeDeM-ODT Diagram

**3.6 The new model SeDeM-ODT to develop orally disintegrating tablets by direct** 

This innovative tool is the new SeDeM-ODT model which provides the Index of Good Compressibility & Bucodispersibility (IGCB index) obtained from the previous SeDeM method (Aguilar et al, 2011). The IGCB index is composed by 6 factors that indicate whether a mixture of powder lends itself to be subjected to direct compression. Moreover, the index simultaneously indicates whether these tablets are suitable as bucodispersible tablet (disintegration in less than 3 minutes). The new factor, disgregability (Table 10), has three parameters that influence this parameter. The graph now comprises 15 parameters (Figure 9). Factor Parameter Limit value (v) Radius

Table 10. The new factor disgregability is added to the SeDeM expert system to achieve the

Effervescence 0-5 (minutes) 10-0 Disintegration Time with disc (DCD) 0-3(minutes) 10-0

Disintegration Time without disc (DSD) 0-3 (minutes) 10-0

Here we developed an original methodology for the preformulation and powder substance characterization. This method facilitates studies on the design and development of formulations for the production of tablets by direct compression. The SeDeM expert system is a useful tool because, in addition to considering the type of components, it also provides recommendations on intrinsic properties, such as the characteristics and morphology of the particles. We propose that given the accuracy of the information provided by this system, formulations will have a higher probability of being successfully compressed.

This method characterizes the individual components of a formulation and applies a mathematical analysis to determine the exact amount of each in the final formulation.

The formulation provided will be valid for direct compression. This manufacturing procedure offers many advantages from a production perspective. In addition to being faster than other techniques, it is straightforward as it reduces the number of steps during the manufacturing process.

In addition SeDeM has the advantage of providing formulation with the lowest amount of excipients as it combines the API with only one excipient and the standard formula of lubricants, thus avoiding the used of unnecessary excipients, such as diluents, binders and agglutinants.

The information given by the SeDeM system contributes to a Quality by Design Development. Consequently, this innovative tool is consistent with the current requirements of regulatory health authorities such as the FDA and ICH.

## **5. References**


**3** 

Avneet Dhawan

 *India* 

*Lovely Faculty of Technology and Sciences, Lovely Professional University, Punjab,* 

**Parametric Modeling and Prognosis** 

**of Result Based Career Selection Based** 

**on Fuzzy Expert System and Decision Trees** 

An Expert System is a set of programs that manipulate encoded knowledge to solve problems in a specialized domain that normally requires human expertise. The expert's knowledge is obtained from the specialists or other sources of expertise, such as texts,

Year # of expert systems developed

Table 1. Increase in number of expert systems developed yearly (based on Durkin, 1998)

Area systems % of Expert Engineering & manufacturing 35 Business 29 Medicine 11 Environment & Energy 9 Agriculture 5 Telecommunications 4 Government 4 Law 3 Transportation 1

Human computer interaction and web-based intelligent tutoring concepts come into play while implementing an online educational tool whose target is mostly unskilled or novice

**1. Introduction** 

**1.1 Expert system and its applications** 

1985 50 1986 86 1987 1100 1988 2200 1992 12000

Table 2. Applications of expert systems in various fields.

journal articles and databases

sustancias en relación a su viabilidad para la compresión: Diagrama SeDeM. *Farmespaña Industrial*, enero/febrero, pp.58-62, ISSN: 1699-4205.


## **Parametric Modeling and Prognosis of Result Based Career Selection Based on Fuzzy Expert System and Decision Trees**

## Avneet Dhawan

*Lovely Faculty of Technology and Sciences, Lovely Professional University, Punjab, India* 

## **1. Introduction**

34 Expert Systems for Human, Materials and Automation

Helman, J. *Farmacotecnia teórica y práctica*. (1981), Compañía Internacional Continental. ISBN:

Kibbe, A.H. *Handbook of Pharmaceutical Excipients*. (2006) (5th ed), American Pharmaceutical

Muñoz Ruíz, A.; Muñoz Muñoz, N.; Monedero Perales, M.C.; Velasco Antequera, M.V. &

Pérez Lozano, P.; Suñé Negre, J.M.; Miñarro, M.; Roig, M.; Fuster, R.; García Montoya, E.;

Suñé Negre, Pérez Lozano, P.; J.M.; Miñarro, M.; Roig, M.; Fuster, R.; García Montoya, E.;

Rubinstein, M.H. *Pharmaceutical Technology (Tabletting Technology).* (1993), (1st Ed), SA de

Suñé Negre, J.M.; Pérez Lozano, P.; Miñarro, M.; Roig, M.; Fuster, R.; García Montoya, E.;

Suñé Negre JM, Pérez Lozano, P.; J.M.; Miñarro, M.; Roig, M.; Fuster, R.; García Montoya, E.;

formulation. *Eur J Pharm & Biopharm*, 69, pp.1029-1039, ISSN: 0939-6411. Suñé Negre, J.M.; Pérez Lozano, P.; Miñarro, M.; Roig, M.; Fuster, R.; García Montoya, E. &

*Biopharmaceutics and Pharmaceutical Technology,* Barcelona, April 2008. Torres Suárez, A.I. & Camacho Sánchez MA. (1991). Planteamiento de un programa de

powders. *Int J Pharm*, 59, pp.145-154, ISSN: 0378-5173

Wong, L.W & Pilpel N. (1990). The effect of particle shape on the mechanical properties of

*Farmespaña Industrial*, enero/febrero, pp.58-62, ISSN: 1699-4205.

Association. Pharmaceutical Press, ISBN: 0-85369-381-1, London

a granel. Métodos (I). *Ind Farm*, 1, pp. 49-55, ISSN: 0213-5574

*Pharm & Biopharm*, 64, pp. 351-359, ISSN:0939-6411

950-06-5081-9, Méjico 6: 1721.

10.1016/J.EJPB.2011.04.002

ISSN:1575-3409

5574

Ediciones, ISBN:978-0136629580, Madrid

sustancias en relación a su viabilidad para la compresión: Diagrama SeDeM.

Jiménez Castellanos Ballesteros, M.R. (1993). Determinación de la fluidez de sólidos

Hernández, C.; Ruhí, R. & Ticó, J.R. (2006). A new expert system (SeDeM Diagram) for control batch powder formulation and preformulation drug products. *Eur J* 

Hernández, C.; Ruhí, R. & Ticó, J.R. Optimization of parameters of the SeDeM Diagram Expert System: Hausner index (HI) and Relative Humidity (%HR). (2011). Approved April 2011 *Eur J Pharm & Biopharm*. ISSN: 0939-6411. DOI:

Hernández, C.; Ruhí, R. & Ticó, J.R. Nueva metodología de preformulación galénica para la caracterización de sustancias en relación a su viabilidad para la compresión: Método SeDeM. (2005). *Cienc Tecnol Pharm*, 15, 3, pp. 125-136,

Hernández, C.; Ruhí, R. & Ticó, J.R. (2008). Application of the SeDeM Diagram and a new mathematical equation in the design of direct compression tablet

Ticó, J.R. (2008). Characterization of powders to preformulation studies with a new expert system (sedem diagram). *Proceedings of 6th World Meeting on Pharmaceutics,* 

preformulación y formulación de comprimidos. *Ind Farm*, 2, pp. 85-92, ISSN: 0213-

## **1.1 Expert system and its applications**

An Expert System is a set of programs that manipulate encoded knowledge to solve problems in a specialized domain that normally requires human expertise. The expert's knowledge is obtained from the specialists or other sources of expertise, such as texts, journal articles and databases


Table 1. Increase in number of expert systems developed yearly (based on Durkin, 1998)


Table 2. Applications of expert systems in various fields.

Human computer interaction and web-based intelligent tutoring concepts come into play while implementing an online educational tool whose target is mostly unskilled or novice

Parametric Modeling and Prognosis of Result Based

individual needs and interests.

accomplished.

experiment;\

Internet.

The specific goals of the approach are that:

Career Selection Based on Fuzzy Expert System and Decision Trees 37

Students, while doing the experiments online by themselves should be coached just as in the case for a traditional laboratory work where the coach is a human assistant or a teacher. They can be given useful directions and recommendations in the form of messages on the interface. Another aspect of coaching is to adapt the level of the complexity of the experiment to the level of the student. Skilled students can be excluded from some parts of the experiment, where unskilled students or students showing a poor performance can be directed to finish the fundamental parts or repeat the unsuccessful parts of the experiment. This idea coincides with the aim of using adaptive hypermedia for intelligent web-based tutoring tools, where the content of the tutor is changed adaptively to suit the student's



By this way, using an intelligent interface for an online robot-supported experimentation will be justified. The educational contexts to benefit from remote experimentation can be range from mechatronics laboratories to chemistry laboratories. According to the scenario, the students can be directed to complete the levels of the experiment according to their skill

In accordance with the issues and the needs stated, the aim of the work given in this thesis is to build a user assessment and coaching framework for an intelligent interface in use during remote access of labs through the Internet involving telerobotics or teleoperation. The lab

1. The interface should provide the student with "hands on" experimentation by using visual feedback and give the user as much freedom as possible to control the

2. The system should evaluate the user performance, adapt the context to the level of acquired knowledge and skill of the user, and thus intelligently coach him/her to

The main objective of this study is, thus, to develop an intelligent interface that can be used for the Internet access of robot supported laboratory. The main differences from the previously surveyed works that are already present in the literature are that the proposed system learns how to assess based on the user behavior while providing online roboticsenhanced experimentation, and coaches him/her towards the successful achievement of the tasks while evaluating user performances. Thus, the proposed approach is behavior-based task planning of online users by being a combination of concepts borrowed from intelligent

level and be coached without the actual presence of a human assistant or a teacher.

setup can be assisted by either a robot or any device that is connected to the Internet.

successfully do the experiment and get the most out of the experimentation. The concepts and tools borrowed from fields such as web-based intelligent tutoring, humancomputer interaction, user-adapted interaction and Internet telerobotics are necessary for the successful accomplishment of our goals in the education oriented lab access through the

There are also other key aspects for a successful interface, which are:

see the state of the robot and the experimental setup.

users. The users (the students in this context) have to be provided with tools that will be helpful in improving their skills in the targeted area. A successful web based education system should have intelligence to tackle the variation in student skills and backgrounds and it should also be able to adapt its contents according to that variation. These mentioned issues are the main concerns for web-based intelligent tutoring research area. For a robot supported laboratory the skill building is both to learn and to gain experience about the control of the robot involved in the experiment setup and to be successful in carrying out the experimentation that is required for the student in order to gain practical knowledge in the targeted area. In order to adapt the context of the experimentation to the variation in student behaviors, students should be modeled according to their skills and knowledge backgrounds. User modelling is an important aspect of both human computer interaction and web-based intelligent tutoring research areas. AI techniques can be applied to the user modelling for implementation of online experimentation framework to get useful information about the student skill and knowledge level for providing help when necessary and assessing his/her performance.

Examples of the early and famous expert systems

	- Analysis of chemical compunds
	- Rule-based system
	- Diagnosis of human internal diseases
	- Symbolic mathematical analysis

ES are appropriate in domains when/where:


Human computer interaction field deals with enhancing the ways in which users interact with one or more computational machines through design, evaluation and implementation of interactive computing systems. From the perspective of telerobotics or more specifically online robotic experimentation, human computer interaction field deals with providing interfaces for remote users which enable them to do the necessary manipulation successfully. There is a strong need for an intelligent interface for a framework for remote access of robot supported laboratories through the Internet. The two main reasons for that are:


Student evaluation, the first main issue mentioned above, is one of the key issues for a remote experimentation framework. Students who are carrying out the experimentation, online without a human assistant or a teacher, should all be evaluated according to their varying success levels. The interface should possess suitable intelligence to categorize the student according to his or her performance during the course of the experiment and possibly to evaluate whether an increase or decrease in performance is present according to the past performance of the users. Necessary grades can then be given to those students according to the performance category in which they tend to fall.

users. The users (the students in this context) have to be provided with tools that will be helpful in improving their skills in the targeted area. A successful web based education system should have intelligence to tackle the variation in student skills and backgrounds and it should also be able to adapt its contents according to that variation. These mentioned issues are the main concerns for web-based intelligent tutoring research area. For a robot supported laboratory the skill building is both to learn and to gain experience about the control of the robot involved in the experiment setup and to be successful in carrying out the experimentation that is required for the student in order to gain practical knowledge in the targeted area. In order to adapt the context of the experimentation to the variation in student behaviors, students should be modeled according to their skills and knowledge backgrounds. User modelling is an important aspect of both human computer interaction and web-based intelligent tutoring research areas. AI techniques can be applied to the user modelling for implementation of online experimentation framework to get useful information about the student skill and knowledge level for providing help when necessary

and assessing his/her performance.

• Rule-based system

• MYCYSMA - MIT (1971)

• there are no established theories

• the domain is highly specific

experimentation successfully.

• DENDRAL - Stanford Univ. (1965) • Analysis of chemical compunds

Examples of the early and famous expert systems

• CADACEUS - Univ. of Pittsburgh (1970) • Diagnosis of human internal diseases

• Symbolic mathematical analysis ES are appropriate in domains when/where:

• the information is fuzzy, inexact or incomplete

• human expertise is scarce or in high demand, but recognized experts exist

laboratories through the Internet. The two main reasons for that are:

according to the performance category in which they tend to fall.

Human computer interaction field deals with enhancing the ways in which users interact with one or more computational machines through design, evaluation and implementation of interactive computing systems. From the perspective of telerobotics or more specifically online robotic experimentation, human computer interaction field deals with providing interfaces for remote users which enable them to do the necessary manipulation successfully. There is a strong need for an intelligent interface for a framework for remote access of robot supported

1. The need for intelligently coaching the student to achieve the goals of the

2. The need for evaluating student's performance while carrying out the experiment. Student evaluation, the first main issue mentioned above, is one of the key issues for a remote experimentation framework. Students who are carrying out the experimentation, online without a human assistant or a teacher, should all be evaluated according to their varying success levels. The interface should possess suitable intelligence to categorize the student according to his or her performance during the course of the experiment and possibly to evaluate whether an increase or decrease in performance is present according to the past performance of the users. Necessary grades can then be given to those students Students, while doing the experiments online by themselves should be coached just as in the case for a traditional laboratory work where the coach is a human assistant or a teacher. They can be given useful directions and recommendations in the form of messages on the interface. Another aspect of coaching is to adapt the level of the complexity of the experiment to the level of the student. Skilled students can be excluded from some parts of the experiment, where unskilled students or students showing a poor performance can be directed to finish the fundamental parts or repeat the unsuccessful parts of the experiment. This idea coincides with the aim of using adaptive hypermedia for intelligent web-based tutoring tools, where the content of the tutor is changed adaptively to suit the student's individual needs and interests.

There are also other key aspects for a successful interface, which are:


By this way, using an intelligent interface for an online robot-supported experimentation will be justified. The educational contexts to benefit from remote experimentation can be range from mechatronics laboratories to chemistry laboratories. According to the scenario, the students can be directed to complete the levels of the experiment according to their skill level and be coached without the actual presence of a human assistant or a teacher.

In accordance with the issues and the needs stated, the aim of the work given in this thesis is to build a user assessment and coaching framework for an intelligent interface in use during remote access of labs through the Internet involving telerobotics or teleoperation. The lab setup can be assisted by either a robot or any device that is connected to the Internet. The specific goals of the approach are that:


The concepts and tools borrowed from fields such as web-based intelligent tutoring, humancomputer interaction, user-adapted interaction and Internet telerobotics are necessary for the successful accomplishment of our goals in the education oriented lab access through the Internet.

The main objective of this study is, thus, to develop an intelligent interface that can be used for the Internet access of robot supported laboratory. The main differences from the previously surveyed works that are already present in the literature are that the proposed system learns how to assess based on the user behavior while providing online roboticsenhanced experimentation, and coaches him/her towards the successful achievement of the tasks while evaluating user performances. Thus, the proposed approach is behavior-based task planning of online users by being a combination of concepts borrowed from intelligent

Parametric Modeling and Prognosis of Result Based

( ) ( )

Low t 1 t / 10 High t t / 10

= − =

Rule 4 : if x is high and y i

fairly common. The same membership functions are used for all variables.

defined.

Career Selection Based on Fuzzy Expert System and Decision Trees 39

composition, and defuzzification. The defuzzification subprocess is optional. For the sake of example in the following discussion, assume that the variables x, y, and z all take on values in the interval [0, 10], and that we have the following membership functions and rules

> Rule 1 : if x is low and y is low then z is high Rule 2 : if x is low and y is high then z is low Rule 3 : if x is high and y is low then z is low

Notice that instead of assigning a single value to the output variable z, each rule assigns an entire fuzzy subset (low or high). In this example, low (t)+high (t)=1.0 for all t. This is not required, but it is fairly common. The value of t at which low (t) is maximum is the same as the value of t at which high (t) is minimum, and vice-versa. This is also not required, but

A fuzzy rule based expert system contains fuzzy rules in its knowledge base and derives conclusions from the user inputs and fuzzy reasoning process. A fuzzy controller is a knowledge based control scheme in which scaling functions of physical variables are used to cope with uncertainty in process dynamics or the control environment. They must usually predefined membership function and fuzzy inference rules to map numeric data into linguistic variable terms (e.g. very high, young,) and to make fuzzy reasoning work. The linguistic variables are usually defined as fuzzy sets with appropriate membership functions. Recently, many fuzzy systems that automatically derive fuzzy if-then rules from numeric data have been developed. In these systems, prototypes of fuzzy rule bases can then be built quickly without the help of human experts, thus avoiding a development bottleneck. Membership functions still need to be predefined, however, and thus are usually built by human experts or experienced users. The same problem as before then arises: if the experts are not available, then the membership functions cannot be accurately defined, or the fuzzy systems developed may not perform well. A recent methodology was developed to automatically generate membership functions by Hong. et al. this methodology can be

applied to a set of data used for a speaker independent voice recognition application.

on the statistical model, agreed upon by the Academic Council of the University.

The conventional practice of student performance practices used globally is based on the marks obtained in the courses opted. The marks are averaged for an overall estimation of the show of the students. In an advanced system the cumulative assessment is done in a group for awarding the grades based on the cumulative performance index (CPI) evaluated

The attendance is taken as variable A1 to AN (Fig. 1.0) in the respective subjects, the overall attendance AO is calculated on simple averaging function. The evaluated AO is then taken into account for deciding whether the student will be allowed to appear in the examination or the student will be detained. This is based on simple comparison operator of less than or equal to the specified attendance. Once the student satisfies this condition of minimum attendance required, the student is made to appear in the examination. On the basis of evaluation of the answer sheets individualistic marks B1 to BN are derived for subjects 1,2, 3 … N respectively. As in case of attendance, the marks of individual subjects are also averaged to fetch overall

s high then z is high

tutoring, student modeling and Internet robotics. Some important properties of the system can be stated briefly as follows:


Fuzzy approach is most suitable for modelling user behaviours from a pattern matching point of view because of its abilities of generalization over the training data set to deal with the fuzzy nature of the user behaviour data. A rule-based system only on its own would require every combination of possible user behaviour data should be explicitly encoded within. Therefore employing a neural network is a feasible solution to the problem of modelling students while doing an online experimentation by using previously defined behaviour stereotypes.

## **2. Fuzzy expert systems**

A fuzzy expert system is an expert system that uses fuzzy logic instead of Boolean logic. In other words, a fuzzy expert system is a collection of membership functions and rules that are used to reason about data. Unlike conventional expert systems, which are mainly symbolic reasoning engines, fuzzy expert systems are oriented toward numerical processing. The rules in a fuzzy expert system are usually of a form similar to the following:

#### **if x is low and y is high then z medium** =

Where x and y are input variables (names for know data values), z is an output variable (a name for a data value to be computed), low is a membership function (fuzzy subset) defined on x, high is a membership function defined on y, and medium is a membership function defined on z. The part of the rule between the "if" and "then" is the rule's premise or antecedent. This is a fuzzy logic expression that describes to what degree the rule is applicable. The part of the rule following the "then" is the rule's conclusion or consequent. This part of the rule assigns a membership function to each of one or more output variables. Most tools for working with fuzzy expert systems allow more than one conclusion per rule. A typical fuzzy expert system has more than one rule. The entire group of rules is collectively known as a rule base or knowledge base.

#### **2.1 The inference process**

With the definition of the rules and membership functions in hand, we now need to know how to apply this knowledge to specific values of the input variables to compute the values of the output variables. This process is referred to as inferencing. In a fuzzy expert system, the inference process is a combination of four subprocesses: fuzzification, inference,

tutoring, student modeling and Internet robotics. Some important properties of the system

• From the nature of the Internet, the system serves to a diverse number of students each having different knowledge and skill levels. The system is adaptive to these different levels and provides each student with enough assistance for accomplishing the desired

• Assistance provided to the student is in the form of generated messages or mandatory commands such as the repetition of a previously failed step of the experiment. • Students are assigned experiments having different complexity levels according to their

• The system grades students according to their performances, and stores grades and

• The system has an authentication module to ensure security and to recall a previous

Fuzzy approach is most suitable for modelling user behaviours from a pattern matching point of view because of its abilities of generalization over the training data set to deal with the fuzzy nature of the user behaviour data. A rule-based system only on its own would require every combination of possible user behaviour data should be explicitly encoded within. Therefore employing a neural network is a feasible solution to the problem of modelling students while doing an online experimentation by using previously defined

A fuzzy expert system is an expert system that uses fuzzy logic instead of Boolean logic. In other words, a fuzzy expert system is a collection of membership functions and rules that are used to reason about data. Unlike conventional expert systems, which are mainly symbolic reasoning engines, fuzzy expert systems are oriented toward numerical processing. The rules in a fuzzy expert system are usually of a form similar to the following:

**if x is low and y is high then z medium** =

Where x and y are input variables (names for know data values), z is an output variable (a name for a data value to be computed), low is a membership function (fuzzy subset) defined on x, high is a membership function defined on y, and medium is a membership function defined on z. The part of the rule between the "if" and "then" is the rule's premise or antecedent. This is a fuzzy logic expression that describes to what degree the rule is applicable. The part of the rule following the "then" is the rule's conclusion or consequent. This part of the rule assigns a membership function to each of one or more output variables. Most tools for working with fuzzy expert systems allow more than one conclusion per rule. A typical fuzzy expert system has more than one rule. The entire group of rules is

With the definition of the rules and membership functions in hand, we now need to know how to apply this knowledge to specific values of the input variables to compute the values of the output variables. This process is referred to as inferencing. In a fuzzy expert system, the inference process is a combination of four subprocesses: fuzzification, inference,

experiment and getting the necessary knowledge and experience.

can be stated briefly as follows:

past and present performances.

student profiles in a database.

user from the database.

behaviour stereotypes.

**2. Fuzzy expert systems** 

**2.1 The inference process** 

collectively known as a rule base or knowledge base.

composition, and defuzzification. The defuzzification subprocess is optional. For the sake of example in the following discussion, assume that the variables x, y, and z all take on values in the interval [0, 10], and that we have the following membership functions and rules defined.

> ( ) ( ) Low t 1 t / 10 High t t / 10 Rule 1 : if x is low and y is low then z is high Rule 2 : if x is low and y is high then z is low Rule 3 : if x is high and y is low then z is low Rule 4 : if x is high and y i = − = s high then z is high

Notice that instead of assigning a single value to the output variable z, each rule assigns an entire fuzzy subset (low or high). In this example, low (t)+high (t)=1.0 for all t. This is not required, but it is fairly common. The value of t at which low (t) is maximum is the same as the value of t at which high (t) is minimum, and vice-versa. This is also not required, but fairly common. The same membership functions are used for all variables.

A fuzzy rule based expert system contains fuzzy rules in its knowledge base and derives conclusions from the user inputs and fuzzy reasoning process. A fuzzy controller is a knowledge based control scheme in which scaling functions of physical variables are used to cope with uncertainty in process dynamics or the control environment. They must usually predefined membership function and fuzzy inference rules to map numeric data into linguistic variable terms (e.g. very high, young,) and to make fuzzy reasoning work. The linguistic variables are usually defined as fuzzy sets with appropriate membership functions. Recently, many fuzzy systems that automatically derive fuzzy if-then rules from numeric data have been developed. In these systems, prototypes of fuzzy rule bases can then be built quickly without the help of human experts, thus avoiding a development bottleneck. Membership functions still need to be predefined, however, and thus are usually built by human experts or experienced users. The same problem as before then arises: if the experts are not available, then the membership functions cannot be accurately defined, or the fuzzy systems developed may not perform well. A recent methodology was developed to automatically generate membership functions by Hong. et al. this methodology can be applied to a set of data used for a speaker independent voice recognition application.

The conventional practice of student performance practices used globally is based on the marks obtained in the courses opted. The marks are averaged for an overall estimation of the show of the students. In an advanced system the cumulative assessment is done in a group for awarding the grades based on the cumulative performance index (CPI) evaluated on the statistical model, agreed upon by the Academic Council of the University.

The attendance is taken as variable A1 to AN (Fig. 1.0) in the respective subjects, the overall attendance AO is calculated on simple averaging function. The evaluated AO is then taken into account for deciding whether the student will be allowed to appear in the examination or the student will be detained. This is based on simple comparison operator of less than or equal to the specified attendance. Once the student satisfies this condition of minimum attendance required, the student is made to appear in the examination. On the basis of evaluation of the answer sheets individualistic marks B1 to BN are derived for subjects 1,2, 3 … N respectively. As in case of attendance, the marks of individual subjects are also averaged to fetch overall

Parametric Modeling and Prognosis of Result Based

**2.3 Data-driven fuzzy rule based approach** 

comparison, is also given briefly in this section.

**linear logic** 

division or the grades.

should be as friendly as possible.

linguistic terms.

Career Selection Based on Fuzzy Expert System and Decision Trees 41

*User interface:* For communication between users and the fuzzy expert system. The interface

*Membership function base:* A mechanism that presents the membership functions of different

*Fuzzy inference engine:* A program that executes the inference cycle of fuzzy matching, fuzzy

*Knowledge-acquisition facility:* An effective knowledge-acquisition tool for conventional interviewing or automatically acquiring the expert's knowledge, or an effective machinelearning approach to deriving rules and membership functions automatically from training instances, or both. Here the membership functions are stored in a knowledge base (instead of being put in the interface) since by our method, decision rules and membership functions are acquired by a learning method. When users input, facts through the user interface, the fuzzy inference engine automatically reasons using the fuzzy rules and the membership functions, and sends fuzzy or crisp results through the user interface to the users as outputs. In the next section, we propose a general learning method as a knowledge-acquisition facility for automatically deriving membership functions and fuzzy rules from a given set of training instances. Based on the membership functions and the fuzzy rules derived, a

Reasoning based on fuzzy approaches has been successfully applied for the inference of multiple attributes containing imprecise data; in particular, fuzzy rule-based systems (FRBS) which provide intuitive methods of reasoning have enjoyed much success in solving realworld problems. Recent developments in this area also show the availability of FRBS which allow interpretation of the inference in the form of linguistic statements whilst having high accuracy rates. The use of linguistic rule models such as "If assignment is very poor and exam is average then the final result is poor" helps capturing the natural way in which humans make judgements and decisions. Furthermore, historical data that is readily available in certain application domains can be used to build fuzzy models which integrate information from data with expert opinions. It is also important that the designed fuzzy models are interpretable by, and explainable to, the user . This section describes a newly proposed data-driven fuzzy rule induction method that achieves such objectives, and shows how the method can be applied to the classification of student performance. Description of Neuro-Fuzzy Classification (NEFCLASS) algorithm, which will be used later for

**2.4 Inducting primitive machine intelligence in performance analysis and reporting by** 

The present scenario of performance evaluation is on the basis of a linear model where the result of the process is in terms of the division or the grades obtained by the student. The system is not capable of deriving cognitive inherence based on the attendance and the marks obtained. It is left to the student, parent and the employer to derive the performance on the

*Fuzzy rule base:* A mechanism for storing fuzzy rules as expert knowledge.

*Explanation mechanism:* A mechanism that explains the inference process to users. *Working memory:* A storage facility that saves user inputs and temporary results.

corresponding fuzzy inference procedure to process user inputs is developed.

conflict resolution, and fuzzy rule firing according to given facts.

marks BO. On the basis of this BO the result of the student is formulated and a division based on characterization of marks range is done. Mathematically on the basis of overall attendance the students qualify to appear in the examination based on a crisp rule as

$$\begin{array}{rcl} \lambda \times \ \cdot \times \rightarrow \ \{0,1\}, \ \mathsf{w} \mathsf{h} \mathsf{i} \mathsf{e} \mathsf{r} & \mathcal{J}\_{\mathcal{A}}(\mathsf{x}) = \begin{cases} 1, \mathsf{if } \mathsf{x} \in \mathcal{A} \\ 0, \mathsf{if } \mathsf{x} \notin \mathcal{A} \end{cases} \end{array}$$

Fig. 1.

Where X is the eligibility percentage of overall attendance, if the overall attendance is > 65%, *fA*(*x*) is 1, then the student is allowed to appear in the exam.

In an advanced conventional system a grading system is eviscerated which is based on the cumulative indexing of the students. This is also a linear method reporting the output of performance on the basis of comparative grading in a group.

The conventional system adopted by the academic institutions is well endeavored and is time tested. The intelligence or the cognitive performance derivation is lacking. Moreover the logical weaving of attendance and the marks obtained in a subject is not done, the outcome of this results in a standalone performance rating and is also not amicable for the parents to assimilate.

#### **2.2 Architecture of a fuzzy expert system**

Fig. 2 shows the basic architecture of a fuzzy expert system. Individual components are illustrated as follows.

Fig. 2. Architecture of a fuzzy expert system

marks BO. On the basis of this BO the result of the student is formulated and a division based on characterization of marks range is done. Mathematically on the basis of overall attendance

Where X is the eligibility percentage of overall attendance, if the overall attendance is > 65%,

In an advanced conventional system a grading system is eviscerated which is based on the cumulative indexing of the students. This is also a linear method reporting the output of

The conventional system adopted by the academic institutions is well endeavored and is time tested. The intelligence or the cognitive performance derivation is lacking. Moreover the logical weaving of attendance and the marks obtained in a subject is not done, the outcome of this results in a standalone performance rating and is also not amicable for the

Fig. 2 shows the basic architecture of a fuzzy expert system. Individual components are

the students qualify to appear in the examination based on a crisp rule as

*fA*(*x*) is 1, then the student is allowed to appear in the exam.

performance on the basis of comparative grading in a group.

**2.2 Architecture of a fuzzy expert system** 

Fig. 2. Architecture of a fuzzy expert system

Fig. 1.

parents to assimilate.

illustrated as follows.

*User interface:* For communication between users and the fuzzy expert system. The interface should be as friendly as possible.

*Membership function base:* A mechanism that presents the membership functions of different linguistic terms.

*Fuzzy rule base:* A mechanism for storing fuzzy rules as expert knowledge.

*Fuzzy inference engine:* A program that executes the inference cycle of fuzzy matching, fuzzy conflict resolution, and fuzzy rule firing according to given facts.

*Explanation mechanism:* A mechanism that explains the inference process to users.

*Working memory:* A storage facility that saves user inputs and temporary results.

*Knowledge-acquisition facility:* An effective knowledge-acquisition tool for conventional interviewing or automatically acquiring the expert's knowledge, or an effective machinelearning approach to deriving rules and membership functions automatically from training instances, or both. Here the membership functions are stored in a knowledge base (instead of being put in the interface) since by our method, decision rules and membership functions are acquired by a learning method. When users input, facts through the user interface, the fuzzy inference engine automatically reasons using the fuzzy rules and the membership functions, and sends fuzzy or crisp results through the user interface to the users as outputs. In the next section, we propose a general learning method as a knowledge-acquisition facility for automatically deriving membership functions and fuzzy rules from a given set of training instances. Based on the membership functions and the fuzzy rules derived, a corresponding fuzzy inference procedure to process user inputs is developed.

## **2.3 Data-driven fuzzy rule based approach**

Reasoning based on fuzzy approaches has been successfully applied for the inference of multiple attributes containing imprecise data; in particular, fuzzy rule-based systems (FRBS) which provide intuitive methods of reasoning have enjoyed much success in solving realworld problems. Recent developments in this area also show the availability of FRBS which allow interpretation of the inference in the form of linguistic statements whilst having high accuracy rates. The use of linguistic rule models such as "If assignment is very poor and exam is average then the final result is poor" helps capturing the natural way in which humans make judgements and decisions. Furthermore, historical data that is readily available in certain application domains can be used to build fuzzy models which integrate information from data with expert opinions. It is also important that the designed fuzzy models are interpretable by, and explainable to, the user . This section describes a newly proposed data-driven fuzzy rule induction method that achieves such objectives, and shows how the method can be applied to the classification of student performance. Description of Neuro-Fuzzy Classification (NEFCLASS) algorithm, which will be used later for comparison, is also given briefly in this section.

## **2.4 Inducting primitive machine intelligence in performance analysis and reporting by linear logic**

The present scenario of performance evaluation is on the basis of a linear model where the result of the process is in terms of the division or the grades obtained by the student. The system is not capable of deriving cognitive inherence based on the attendance and the marks obtained. It is left to the student, parent and the employer to derive the performance on the division or the grades.

Parametric Modeling and Prognosis of Result Based

**Step 3.** construct the initial decision table; **Step 4.** simplify the initial decision table;

Fig. 3. Learning activity.

Fig. 4. Structure of WSBA Approach

**Step 6.** derive decision rules from the decision table.

**3.2 Weighted Subset Hood-Based Algorithm (WSBA)** 

**Step 2.** construct initial membership functions for input attributes;

**Step 5.** rebuild membership functions in the simplification process;

Career Selection Based on Fuzzy Expert System and Decision Trees 43

Simplicity in generating fuzzy rules and the ability to produce high classification accuracy are the main objectives in the development of WSBA. To achieve these objectives, fuzzy

subset hood measures and weighted linguistic fuzzy modelling are employed.

## **3. The logical engine**

Several approaches using fuzzy techniques have been proposed to provide a practical method for evaluating student academic performance. However, these approaches are largely based on expert opinions and are difficult to explore and utilize valuable information embedded in collected data. This paper proposes a new method for evaluating student academic performance based on data-driven fuzzy rule induction. A suitable fuzzy inference mechanism and associated Rule Induction Algorithm is given. The new method has been applied to perform *Criterion-Referenced Evaluation (CRE)* and comparisons are made with typical existing methods, revealing significant advantages of the present work. The new method has also been applied to perform *Norm- Referenced Evaluation (NRE)*, demonstrating its potential as an extended method of evaluation that can produce new and informative scores based on information gathered from data. The need of the hour is to device a proposition where, an intelligent system sits inside the conventional system and deduce decisions based on the attendance and the marks obtained. Two sets are formulated Set A is for attendance and Set B is for marks obtained in the examination by the student.

> ( ) { } ( ) ( ) ( ) ( ) ( ) : X 0, 1 , where 1 if x is totally in A; Eligible 0 if x is not in A; Not Eligible 0 *A A A A µ x µ x µ x µ x* → = = < 1 if x is partly in A. <

#### **3.1 The knowledge acquisition facility**

A new learning method for automatically deriving fuzzy rules and membership functions from a given set of training instances is proposed here as the knowledge acquisition facility.

## **3.1.1 Notation and definitions**

In a training instance, both input and desired output are known. For a m-dimensional input space, the ith training example can then be described as

$$(\mathbf{x\_{il'}}, \mathbf{x\_{i2,}}, \dots, \mathbf{x\_{im'}}, \mathbf{y\_i})\_{'}$$

where xir (1 < *r < m)* is the rth attribute value of the ith training example and yi is the output value of the ith training example.

For example, assume an insurance company decides *insurance fees* according to two attributes: *age a*nd *property.* If the insurance company evaluates and decides the insurance fee for a person of age 20 possessing property worth \$30000 should be \$1000, then the example is represented as (age = 20, property = \$30 000, insurance fee = \$1000).

#### **3.1.2 The algorithm**

The learning activity is shown in Fig. 3

A set of training instances is collected from the environment. Our task here is to generate automatically reasonable membership functions and appropriate decision rules from these training data, so that they can represent important features of the data set. The proposed learning algorithm can be divided into five main steps:

**Step 1.** cluster and fuzzify the output data;


Fig. 3. Learning activity.

42 Expert Systems for Human, Materials and Automation

Several approaches using fuzzy techniques have been proposed to provide a practical method for evaluating student academic performance. However, these approaches are largely based on expert opinions and are difficult to explore and utilize valuable information embedded in collected data. This paper proposes a new method for evaluating student academic performance based on data-driven fuzzy rule induction. A suitable fuzzy inference mechanism and associated Rule Induction Algorithm is given. The new method has been applied to perform *Criterion-Referenced Evaluation (CRE)* and comparisons are made with typical existing methods, revealing significant advantages of the present work. The new method has also been applied to perform *Norm- Referenced Evaluation (NRE)*, demonstrating its potential as an extended method of evaluation that can produce new and informative scores based on information gathered from data. The need of the hour is to device a proposition where, an intelligent system sits inside the conventional system and deduce decisions based on the attendance and the marks obtained. Two sets are formulated Set A is for attendance and Set B is for marks obtained in the examination by the student.

> ( ) ( ) ( ) ( )

1 if x is totally in A; Eligible

= =

0 if x is not in A; Not Eligible

A new learning method for automatically deriving fuzzy rules and membership functions from a given set of training instances is proposed here as the knowledge acquisition facility.

In a training instance, both input and desired output are known. For a m-dimensional input

( ) , , ... , ; , **il i2 im i xx x y**

where xir (1 < *r < m)* is the rth attribute value of the ith training example and yi is the output

For example, assume an insurance company decides *insurance fees* according to two attributes: *age a*nd *property.* If the insurance company evaluates and decides the insurance fee for a person of age 20 possessing property worth \$30000 should be \$1000, then the

A set of training instances is collected from the environment. Our task here is to generate automatically reasonable membership functions and appropriate decision rules from these training data, so that they can represent important features of the data set. The proposed

example is represented as (age = 20, property = \$30 000, insurance fee = \$1000).

**3. The logical engine** 

( ) { }

→

: X 0, 1 , where

< 1 if x is partly in A. <

space, the ith training example can then be described as

learning algorithm can be divided into five main steps:

*A A*

*µ x µ x*

( )

**3.1 The knowledge acquisition facility** 

*A*

**3.1.1 Notation and definitions** 

value of the ith training example.

The learning activity is shown in Fig. 3

**Step 1.** cluster and fuzzify the output data;

**3.1.2 The algorithm** 

*µ x*

0

*A*

*µ x*

## **3.2 Weighted Subset Hood-Based Algorithm (WSBA)**

Simplicity in generating fuzzy rules and the ability to produce high classification accuracy are the main objectives in the development of WSBA. To achieve these objectives, fuzzy subset hood measures and weighted linguistic fuzzy modelling are employed.

Fig. 4. Structure of WSBA Approach

Parametric Modeling and Prognosis of Result Based

obtain the decision class for the training data.

coursework grading for instances).

represent the training data (SAP50A).

of the methods. For instance:

approaches.

**3.5 Criterion Referenced Evaluation (CRE)** 

account:

example).

Career Selection Based on Fuzzy Expert System and Decision Trees 45

implementation. The objective of the experiment involving CRE is to provide evidence that the proposed algorithm will produce results similar to the original grades obtained using

The objective of the experiment involving NRE is to show that WSBA is able to produce grades that can be used to provide additional information on the achievement of the students. In conducting these experiments, the following aspects have been taken into

In data-driven rule based systems, decision classes of the training instances are typically those given by experts. In students' performance evaluation, such decisions are normally given by experts based on an aggregation of numerical crisp scores. This method is used to

The small training data (SAP50A and SAP50B) is used as an example and in the form of numerical crisp scores, which is the most popular way to measure student performance. Note that the fuzzy approach allows the possibility of utilizing data in the form of fuzzy values such as those proposed in or in terms of linguistic labels that represent the fuzzy sets. In such cases, the decision class for the training data is determined by fuzzy values (see for

To avoid confusion, 'original score/grade' in this section will refer to the score and grade obtained from the use of the standard statistical mean and 'new score/grade' will refer to the score or grade obtained from existing fuzzy approaches, including WSBA and NEFCLASS. Note that both datasets used include only numerical scores, to facilitate comparison with other approaches. This need not be the case in general, the scores of individual assessment components may be given in fuzzy terms (as often the case for

NEFCLASS is used for further comparison, employing a fuzzy rule-based approach. The dataset used for the purpose of training WSBA and NEFCLASS models is a set of student performance records (labeled SAP50A). It consists of 50 instances, involving three conditional attributes: assignment, test and final exam, and five possible classification outcomes: Unsatisfactory (E), Satisfactory (D), Average (C), Good (B) and Excellent (A). Note that the term 'Average' describing students' performance used in this paper is not referring to the statistical average. For the sake of simplicity, only five linguistic labels similar to the classification outcomes are used to represent student achievements. The fuzzy partitions and labels are based on expert opinions representing the students' performance. The primary assumption is that the partitions chosen by experts are those best possible to

Clearly, better fuzzification, if available will help improve the experimental results reported below. Note that the given definition of the fuzzy sets is obtained solely on the basis of the normal distribution of the crisp marks given. This ensures their comparison with other

The classification of the grades in this experiment is based on an interval that refers to the level of performance given by experts. To facilitate a fair comparison, the same dataset consisting of 15 instances and having the same features as the training dataset is used for all

statistical methods, if an ideal and representative training data is available.

This method does not require any threshold value and generates a fixed number of rules according to the number of classes of interest (i.e. one rule will be created for each class). In the process of generating fuzzy rules, linguistic terms that have a weight greater than zero will automatically be promoted to become part of the antecedents of the resulting fuzzy rules. Any linguistic term that has a weight equal to 0 will of course be removed from the fuzzy rule. This will make the rules simpler than the original default rules. In running WSBA for classification tasks, the concluding classification will be that of the rule whose overall weight is the highest amongst all. The structure of WSBA approach is shown in Figure 4. Example applications of WSBA can be found in.

## **3.3 Neuro-Fuzzy Classification (NEFCLASS)**

Neuro-Fuzzy Classification (NEFCLASS) is an FRBS which combines a neural network learning approach with a fuzzy rule-based inference method . NEFCLASS can be encoded as a three-layer feedforward neural network. The first layer represents the fuzzy input variables, the second layer represents the fuzzy rulesets and the third layer represents the output variables. The functional units in this network implement t-norms and t-conorms, replacing the activation functions that are commonly used in conventional neural networks. NEFCLASS is a data-driven FRBS that has the ability to create fuzzy membership functions and fuzzy rules automatically from training instances. Prior knowledge in the form of fuzzy rules can also be added to the rule base and used alongside new rules created using the training dataset.

Fuzzy rules are generated based on overlapping rectangular clusters that are created by the grid representing fuzzy sets for the conditional attributes. Clusters that cover areas where training data is located are added to the emerging rule-base. The system allows the user to choose the maximum number of rules, otherwise the number of rules are restricted to that of just the best performing ones. The firing strength of each rule is used to reach the conclusion on the decision class of new observations.

The number of partitions and the shape of membership functions of the conditional attributes are user-defined. The rule learning process can be started, for example, using a fixed number of equally distributed triangular membership functions. A simple heuristic method is used for the optimization of membership functions. The optimization process results in changes to the membership function's shape by making the supports of the fuzzy set larger or smaller. Constraints can be employed in the optimization process to make sure that the fuzzy sets overlap each other.

NEFCLASS has undergone through several refinements over the years. For example, to enhance the interpretability of the induced fuzzy rules, NEFCLASS offers additional features such as rule pruning and variable pruning. The system has also been tested not only for classification of benchmark datasets but also for real world problems such as presented in.

#### **3.4 Experimental results**

The experiments presented in this section served as examples to illustrate the potential of WSBA for the evaluation of student performance. Note that a wide range of assessment methods are available and have been used (see for example ), depending on the purpose to conduct the assessment. In this paper, only CRE and NRE are considered for the

This method does not require any threshold value and generates a fixed number of rules according to the number of classes of interest (i.e. one rule will be created for each class). In the process of generating fuzzy rules, linguistic terms that have a weight greater than zero will automatically be promoted to become part of the antecedents of the resulting fuzzy rules. Any linguistic term that has a weight equal to 0 will of course be removed from the fuzzy rule. This will make the rules simpler than the original default rules. In running WSBA for classification tasks, the concluding classification will be that of the rule whose overall weight is the highest amongst all. The structure of WSBA approach is shown in

Neuro-Fuzzy Classification (NEFCLASS) is an FRBS which combines a neural network learning approach with a fuzzy rule-based inference method . NEFCLASS can be encoded as a three-layer feedforward neural network. The first layer represents the fuzzy input variables, the second layer represents the fuzzy rulesets and the third layer represents the output variables. The functional units in this network implement t-norms and t-conorms, replacing the activation functions that are commonly used in conventional neural networks. NEFCLASS is a data-driven FRBS that has the ability to create fuzzy membership functions and fuzzy rules automatically from training instances. Prior knowledge in the form of fuzzy rules can also be added to the rule base and used alongside new rules created using the

Fuzzy rules are generated based on overlapping rectangular clusters that are created by the grid representing fuzzy sets for the conditional attributes. Clusters that cover areas where training data is located are added to the emerging rule-base. The system allows the user to choose the maximum number of rules, otherwise the number of rules are restricted to that of just the best performing ones. The firing strength of each rule is used to reach the conclusion

The number of partitions and the shape of membership functions of the conditional attributes are user-defined. The rule learning process can be started, for example, using a fixed number of equally distributed triangular membership functions. A simple heuristic method is used for the optimization of membership functions. The optimization process results in changes to the membership function's shape by making the supports of the fuzzy set larger or smaller. Constraints can be employed in the optimization process to make sure

NEFCLASS has undergone through several refinements over the years. For example, to enhance the interpretability of the induced fuzzy rules, NEFCLASS offers additional features such as rule pruning and variable pruning. The system has also been tested not only for classification of benchmark datasets but also for real world problems such as

The experiments presented in this section served as examples to illustrate the potential of WSBA for the evaluation of student performance. Note that a wide range of assessment methods are available and have been used (see for example ), depending on the purpose to conduct the assessment. In this paper, only CRE and NRE are considered for the

Figure 4. Example applications of WSBA can be found in.

**3.3 Neuro-Fuzzy Classification (NEFCLASS)** 

on the decision class of new observations.

that the fuzzy sets overlap each other.

training dataset.

presented in.

**3.4 Experimental results** 

implementation. The objective of the experiment involving CRE is to provide evidence that the proposed algorithm will produce results similar to the original grades obtained using statistical methods, if an ideal and representative training data is available.

The objective of the experiment involving NRE is to show that WSBA is able to produce grades that can be used to provide additional information on the achievement of the students. In conducting these experiments, the following aspects have been taken into account:

In data-driven rule based systems, decision classes of the training instances are typically those given by experts. In students' performance evaluation, such decisions are normally given by experts based on an aggregation of numerical crisp scores. This method is used to obtain the decision class for the training data.

The small training data (SAP50A and SAP50B) is used as an example and in the form of numerical crisp scores, which is the most popular way to measure student performance. Note that the fuzzy approach allows the possibility of utilizing data in the form of fuzzy values such as those proposed in or in terms of linguistic labels that represent the fuzzy sets. In such cases, the decision class for the training data is determined by fuzzy values (see for example).

To avoid confusion, 'original score/grade' in this section will refer to the score and grade obtained from the use of the standard statistical mean and 'new score/grade' will refer to the score or grade obtained from existing fuzzy approaches, including WSBA and NEFCLASS. Note that both datasets used include only numerical scores, to facilitate comparison with other approaches. This need not be the case in general, the scores of individual assessment components may be given in fuzzy terms (as often the case for coursework grading for instances).

## **3.5 Criterion Referenced Evaluation (CRE)**

NEFCLASS is used for further comparison, employing a fuzzy rule-based approach. The dataset used for the purpose of training WSBA and NEFCLASS models is a set of student performance records (labeled SAP50A). It consists of 50 instances, involving three conditional attributes: assignment, test and final exam, and five possible classification outcomes: Unsatisfactory (E), Satisfactory (D), Average (C), Good (B) and Excellent (A). Note that the term 'Average' describing students' performance used in this paper is not referring to the statistical average. For the sake of simplicity, only five linguistic labels similar to the classification outcomes are used to represent student achievements. The fuzzy partitions and labels are based on expert opinions representing the students' performance. The primary assumption is that the partitions chosen by experts are those best possible to represent the training data (SAP50A).

Clearly, better fuzzification, if available will help improve the experimental results reported below. Note that the given definition of the fuzzy sets is obtained solely on the basis of the normal distribution of the crisp marks given. This ensures their comparison with other approaches.

The classification of the grades in this experiment is based on an interval that refers to the level of performance given by experts. To facilitate a fair comparison, the same dataset consisting of 15 instances and having the same features as the training dataset is used for all of the methods. For instance:

Parametric Modeling and Prognosis of Result Based

Graphically it can be represented as

Fig. 5. Non-linear membership degree

applied where z is made constant as k.

**5. Variables deduction** 

deduced as follows:

inference and is as

direction for probabilistic performance modeling of the student.

where x∈X

where x∈X

Career Selection Based on Fuzzy Expert System and Decision Trees 47

μ μμ μ μ *A* ∩ = *B x min [ A x B x ] A x B x* () () () () () , , = ∩

μ μμ μ μ *A* ∪ ∪ *B x = max [ A x , B x ] = A x B x ,* () () () () ()

The decisions DES41 and DES42 are derivative of the non-linear vector running simultaneously on the set A and set B for attendance and marks respectively. These high end decisions DES4x are being used for the suggestions to be included in the report of the student. These not only make this Communiqué system absolutely unique but also enthrall

Mathematically the non-linear (dependent) vector is designed on the Sugeno Fuzzy

*IF x is A AND y is B THEN z is f x y* where *x*, *y* and *z* are linguistic variables; *A* and *B* are fuzzy sets on universe of discourses X and *Y*, respectively; and *f*(*x*, *y*) is a mathematical function. The zero order fuzzy model is

 

( )

,

In the decision support system, the linear and non linear decisions are inferred through the decision vectors devised on the marks obtained and attendance of the student. So the different linguistic variables have been undertaken for the performance analysis and are

1. The linguistic variables undertaken for the performance reporting of a student at the initial stage are DES1 and DES11 derived from the logical decision agent. These two variables are used for the Gender Confirmation of the student. If the sex of the student

The highest memberships will be drawn out by the union of two sets as


It can be seen that the conventional fuzzy approaches produce different scores from the original (that is obtained by statistical mean). Thus, it is expected that when the new scores are translated into new grades, some of them may be different from the original grades. In particular, the results returned by the method of Biswas (1995), give rise to unexpected new scores such as case 10 where the original score of 61.67 (grade B) was downgraded to 35 (grade D). This is due to the approximation that is used in creating mid-grade points, and partly due to the use of fuzzy input values. Note that the use of mid-grade points has also resulted in a minimum score of 12.5 and a maximum score of 87.5, narrower than the original range.

Using Chen and Lee's method, all of the new scores are higher than the original. This is due to the use of maximum values of the degree of satisfaction created for each level of achievement. As for the results produced by Law's method, it is expected that the new scores will be different because the expected value for each grade has been predefined in advance according to the percentage of students who will receive a certain grade. Thus, results produced by this method may not reflect the students' true performance and they will be different if the expert evaluator changes the setting for the percentage.

By using the data-driven fuzzy rule-based approaches, fuzzy membership values obtained from fuzzy rules can be used to determine the new grade. Thus, it can be observed that the use of membership values in describing a student result has several advantages.

First, these membership values can be interpreted as how strong the student's performance belongs to a specific grade. This can be very useful in differentiating smoothly student performances over boundary cases, giving a second opinion in deciding on borderline performances.

Second, with the use of fuzzy values, further analysis of estimated performance can be carried out directly, without the need for fuzzification.

Third, the success of those methods in performing CRE will allow them to be used for NRE. This also provides the possibility that student performance evaluation can be carried out properly using fuzzy values and linguistic terms (Good, Excellent, etc.) rather than the traditional numerical crisp values.

## **4. Design of non-linear decision vector**

The innovation in the present work is to create a logical mechanism which binds the attendance in the class room and the marks obtained in the examination by the student and to infer the decisions weaved on the sets A and B. This juxtapose will endeavor the performance of the student at the said instance and will also delineate the seed for prognostic modeling of futuristic performance of the students.

Mathematically, lowest memberships will be figured out by the intersection of two sets as

$$
\mu A \cap B(\infty) = \min \left\{ \mu A\left(\infty\right), \mu B\left(\infty\right) \right\} = \mu A\left(\infty\right) \cap \mu B\left(\infty\right),
$$

where x∈X

46 Expert Systems for Human, Materials and Automation

It can be seen that the conventional fuzzy approaches produce different scores from the original (that is obtained by statistical mean). Thus, it is expected that when the new scores are translated into new grades, some of them may be different from the original grades. In particular, the results returned by the method of Biswas (1995), give rise to unexpected new scores such as case 10 where the original score of 61.67 (grade B) was downgraded to 35 (grade D). This is due to the approximation that is used in creating mid-grade points, and partly due to the use of fuzzy input values. Note that the use of mid-grade points has also resulted in a minimum score of 12.5 and a maximum score of 87.5, narrower than the

Using Chen and Lee's method, all of the new scores are higher than the original. This is due to the use of maximum values of the degree of satisfaction created for each level of achievement. As for the results produced by Law's method, it is expected that the new scores will be different because the expected value for each grade has been predefined in advance according to the percentage of students who will receive a certain grade. Thus, results produced by this method may not reflect the students' true performance and they

By using the data-driven fuzzy rule-based approaches, fuzzy membership values obtained from fuzzy rules can be used to determine the new grade. Thus, it can be observed that the

First, these membership values can be interpreted as how strong the student's performance belongs to a specific grade. This can be very useful in differentiating smoothly student performances over boundary cases, giving a second opinion in deciding on borderline

Second, with the use of fuzzy values, further analysis of estimated performance can be

Third, the success of those methods in performing CRE will allow them to be used for NRE. This also provides the possibility that student performance evaluation can be carried out properly using fuzzy values and linguistic terms (Good, Excellent, etc.) rather than the

The innovation in the present work is to create a logical mechanism which binds the attendance in the class room and the marks obtained in the examination by the student and to infer the decisions weaved on the sets A and B. This juxtapose will endeavor the performance of the student at the said instance and will also delineate the seed for

Mathematically, lowest memberships will be figured out by the intersection of two sets as

will be different if the expert evaluator changes the setting for the percentage.

use of membership values in describing a student result has several advantages.

carried out directly, without the need for fuzzification.

prognostic modeling of futuristic performance of the students.

traditional numerical crisp values.

**4. Design of non-linear decision vector** 

Marks Grade Level of achievement

0-25 E Unsatisfactory 26-45 D Satisfactory 46-55 C Average 56-75 B Good 76-100 A Excellent

original range.

performances.

The highest memberships will be drawn out by the union of two sets as

$$\mu A \cup B(\infty) \doteq \max \{ \mu A(\infty), \mu B(\infty) \} = \mu A(\infty) \cup \mu B(\infty) \dots$$

where x∈X Graphically it can be represented as

Fig. 5. Non-linear membership degree

The decisions DES41 and DES42 are derivative of the non-linear vector running simultaneously on the set A and set B for attendance and marks respectively. These high end decisions DES4x are being used for the suggestions to be included in the report of the student. These not only make this Communiqué system absolutely unique but also enthrall direction for probabilistic performance modeling of the student.

Mathematically the non-linear (dependent) vector is designed on the Sugeno Fuzzy inference and is as

$$\begin{aligned} \label{eq:H} & IF & \quad \text{x is } A\\ \begin{array}{ll} \text{AND} & \quad y \text{ is } B\\ \text{THEN} & \quad z \text{ is } f\left(x, y\right) \end{array} \end{aligned}$$

where *x*, *y* and *z* are linguistic variables; *A* and *B* are fuzzy sets on universe of discourses X and *Y*, respectively; and *f*(*x*, *y*) is a mathematical function. The zero order fuzzy model is applied where z is made constant as k.

## **5. Variables deduction**

In the decision support system, the linear and non linear decisions are inferred through the decision vectors devised on the marks obtained and attendance of the student. So the different linguistic variables have been undertaken for the performance analysis and are deduced as follows:

1. The linguistic variables undertaken for the performance reporting of a student at the initial stage are DES1 and DES11 derived from the logical decision agent. These two variables are used for the Gender Confirmation of the student. If the sex of the student

Parametric Modeling and Prognosis of Result Based

interpretable whilst maintaining its accuracy.

better fuzzy partition automatically from data.

Academic Publishers, 1998.

Springer, pp.3-98, 1999.

West Publishing Company, 1996.

rules and high performance.

**7. References** 

2005.

Career Selection Based on Fuzzy Expert System and Decision Trees 49

In particular, interpretability of learned fuzzy rules has always been regarded as a very important factor in FRBS but has not been sufficiently addressed in this paper. Thus, further research should include this very important issue. As an approximate modellling approach, WSBA has the advantage in producing fuzzy systems of high classification accuracy, but the use of crisp weights to modify fuzzy terms is rather unnatural and may lead to confusion regarding the semantics of the resulting systems. However, the structure of WSBA rulesets enables the system model to be adapted with fuzzy quantifiers , making the model more

Also, the creation of fuzzy partitions to be used for WSBA are currently based on expert opinion and partly from statistical information on the training data. The fuzzification is not in any way optimized. Further research should include the use of methods that generate better fuzzy partition automatically from data.The proposed method also provides room for other improvements. In particular, interpretability of learned fuzzy rules has always been regarded as a very important factor in FRBS but has not been sufficiently addressed in this paper. Thus, further research should include this very important issue. As an approximate modellling approach, WSBA has the advantage in producing fuzzy systems of high classification accuracy, but the use of crisp weights to modify fuzzy terms is rather unnatural and may lead to confusion regarding the semantics of the resulting systems. However, the structure of WSBA rulesets enables the system model to be adapted with fuzzy quantifiers , making the model more interpretable whilst maintaining its accuracy. Also, the creation of fuzzy partitions to be used for WSBA are currently based on expert opinion and partly from statistical information on the training data. The fuzzification is not in any way optimized. Further research should include the use of methods that generate

Thus, The proposed approach can significantly reduce the time and effort needed for the performance evaluation of large number of students and help build intelligent communiqué system. Based on membership functions and fuzzy rules derived, a corresponding fuzzy inference procedure to process the inputs is developed. Embedding the decision support system fuzzy logic and decision trees , we found that our model gives a rational result, few

Y. Caballero, R. Bello, A. Taboada, A. Nowe, M. Garcia, and G. Casas, "A new measure

K. Cios, W. Pedrycz, and R. Swiniarski, *Data Mining Methods forKnowledge Discovery*, Kluwer

C.W. Holsapple and A.B. Whinston, *Decision Support Systems: AKnowledge-Based Approach*,

X. Hu, "Using rough sets theory and database operations to construct a good ensemble of classifiers for data mining application", *Proc.IEEE ICDM*, pp.233-240, 2001. J. Komorowski, L. Polkowski, and A. Skowron, "Rough sets: A tutorial", In S.K. Pal and A.

Skowron (eds.), *Rough Fuzzy Hybridization: A New Trend in Decision-Making*,

Z. Chen, *Computational Intelligence for Decision Support*, CRC Press, 2000.

based in the rough set theory to estimate the training set quality", *Proc.8th Int. Symp. Symbolic and Numeric Algorithms for Scientific Computing*, pp.133-140, 2006. B. Chapman and B. Hall, *Learning Content Management System.* Brandonhall.com, New York,

in the student\_master table is found to be "M" then the DES1 is set to "son" and the corresponding linguistic variable DES11 is set to "him". In the similar manner if the entry corresponding sex comes out to be false then DES1 is set to "daughter" and the variable DES11 set to "her". Both of these variables are embedded in the report while giving suggestions to the parents regarding their ward.


## **6. Conclusion**

This paper has presented examples of how a fuzzy rule-based approach can be used for aggregation of student academic performance and helps him in his career selection. It has been shown that the proposed approach has several advantages compared to existing fuzzy techniques for the evaluation of student academic performance.

In CRE, the use of fuzzy membership values to determine the decision is very helpful for the user to understand why the new grade was awarded. In CRE, the proposed method has the potential to be developed further for use as an extended method of evaluation by providing new grades that refer to achievements of other groups. The membership values produced by this method are also more meaningful compared to the values produced by statistical standardized-score.

However, it is worth noting that the newly proposed fuzzy approach is not to replace the traditional method of evaluation; instead it is meant to help strengthen the system that is commonly in use, by providing additional information for decision making by the user.

In this paper, WSBA is proposed to be employed for this purpose because of the simplicity of the method. It has been shown that although WSBA employs a simple approach, the proposed method is able to provide classification similar to that produced by more sophisticated algorithm such as NEFCLASS. Of course, more complex fuzzy rule-based methods such as those based on Evolutionary Computation, Fuzzy Clustering and Neural Networks may also be used. However, the simpler approach has an advantage in terms of transparency and understandability of the methods and its results. The proposed method also provides room for other improvements.

In particular, interpretability of learned fuzzy rules has always been regarded as a very important factor in FRBS but has not been sufficiently addressed in this paper. Thus, further research should include this very important issue. As an approximate modellling approach, WSBA has the advantage in producing fuzzy systems of high classification accuracy, but the use of crisp weights to modify fuzzy terms is rather unnatural and may lead to confusion regarding the semantics of the resulting systems. However, the structure of WSBA rulesets enables the system model to be adapted with fuzzy quantifiers , making the model more interpretable whilst maintaining its accuracy.

Also, the creation of fuzzy partitions to be used for WSBA are currently based on expert opinion and partly from statistical information on the training data. The fuzzification is not in any way optimized. Further research should include the use of methods that generate better fuzzy partition automatically from data.The proposed method also provides room for other improvements. In particular, interpretability of learned fuzzy rules has always been regarded as a very important factor in FRBS but has not been sufficiently addressed in this paper.

Thus, further research should include this very important issue. As an approximate modellling approach, WSBA has the advantage in producing fuzzy systems of high classification accuracy, but the use of crisp weights to modify fuzzy terms is rather unnatural and may lead to confusion regarding the semantics of the resulting systems.

However, the structure of WSBA rulesets enables the system model to be adapted with fuzzy quantifiers , making the model more interpretable whilst maintaining its accuracy. Also, the creation of fuzzy partitions to be used for WSBA are currently based on expert opinion and partly from statistical information on the training data. The fuzzification is not in any way optimized. Further research should include the use of methods that generate better fuzzy partition automatically from data.

Thus, The proposed approach can significantly reduce the time and effort needed for the performance evaluation of large number of students and help build intelligent communiqué system. Based on membership functions and fuzzy rules derived, a corresponding fuzzy inference procedure to process the inputs is developed. Embedding the decision support system fuzzy logic and decision trees , we found that our model gives a rational result, few rules and high performance.

## **7. References**

48 Expert Systems for Human, Materials and Automation

2. The degree of membership to attendance set A will formulate the linguistic variables DES21 and DES22. The DES21 is derived from the nested block of logical decision agent based on the membership in the set. The attendance of the student can be excellent, good, moderate or non-confirming depending on the regularity of the student. DES22 is the extended decision for suggesting the actions/ modifications to be undertaken by the student and the parent with respect to the overall attendance. While formulating the suggestion regarding attendance the decision variables DES1 and DES11 are also

3. On the basis of degree of membership to the marks obtained set B, DES31 and DES32 are formulated. Depending upon the marks obtained by the student, the membership assigns PASS or FAIL status to the student. Set B constitutes the pass students. DES31 determines whether the performance of the student is excellent, good, fair or non confirming. Variable DES32 is used for the suggestion based on the academic performance. It comprises of the individualistic decision based on the linear logical decision agents for attendance and marks obtained. While formulating the suggestion regarding marks DES1,

4. DES41 and DES42 are the decisions derived from the non linear vector running simultaneously on the set A and set B for attendance and marks respectively. These decisions are embedded for the suggestions regarding career selection given to the

This paper has presented examples of how a fuzzy rule-based approach can be used for aggregation of student academic performance and helps him in his career selection. It has been shown that the proposed approach has several advantages compared to existing fuzzy

In CRE, the use of fuzzy membership values to determine the decision is very helpful for the user to understand why the new grade was awarded. In CRE, the proposed method has the potential to be developed further for use as an extended method of evaluation by providing new grades that refer to achievements of other groups. The membership values produced by this method are also more meaningful compared to the values produced by statistical

However, it is worth noting that the newly proposed fuzzy approach is not to replace the traditional method of evaluation; instead it is meant to help strengthen the system that is commonly in use, by providing additional information for decision making by the user. In this paper, WSBA is proposed to be employed for this purpose because of the simplicity of the method. It has been shown that although WSBA employs a simple approach, the proposed method is able to provide classification similar to that produced by more sophisticated algorithm such as NEFCLASS. Of course, more complex fuzzy rule-based methods such as those based on Evolutionary Computation, Fuzzy Clustering and Neural Networks may also be used. However, the simpler approach has an advantage in terms of transparency and understandability of the methods and its results. The proposed method

DES11, SUBSHORT, DES21 and DES22 are embedded as per the prerequisite.

parents and are implanted at the end of the student's report.

techniques for the evaluation of student academic performance.

giving suggestions to the parents regarding their ward.

embedded wherever required.

**6. Conclusion** 

standardized-score.

also provides room for other improvements.

in the student\_master table is found to be "M" then the DES1 is set to "son" and the corresponding linguistic variable DES11 is set to "him". In the similar manner if the entry corresponding sex comes out to be false then DES1 is set to "daughter" and the variable DES11 set to "her". Both of these variables are embedded in the report while


**4** 

Petr Sosnin

*Russia* 

**Question-Answer Shell** 

*Ulyanovsk State Technical University,* 

**for Personal Expert Systems** 

In the near future a ubiquitous computerization of all spheres of the modern human activity, including various forms of the collective activity, will lead to conditions of a life when all population of the Earth will be involved in interactions with computers. Therefore, in usages of computers by the person it is necessary to aspire to a naturalness of such attitudes. The naturalness should be achieved in that sense that any usage of a computer should be

Any activity is a naturally-artificial process created on the base of a definite set of precedents the samples of which are extracted from the appropriate experience and its models. Such role of precedents is explained with the help of the following definition: "précédents are actions or decisions that have already happened in the past and which can be referred to and justified as an example that can be followed when the similar situation arises"

Accessible samples of precedents are necessary means for the activity but in a general case such means can be insufficiently. If absent means will be found and the necessary activity will be created then the new sample of precedent can be built for the reuse of this activity. Hence, told above entitles to assert that "the creation and reuse of precedents defines the

Each unit of the fulfilled activity must be modeled by the useful way, be investigated and be coded for its reuse as the precedent. In the life all these actions are similar to creating the programs for the building of which a natural language in its algorithmic usage is applied. Moreover such programs as behavioral schemes are built for tasks which have been solved for already created units of the activity. So, any sample of the precedent can be understood as a program which is coded previously at the natural language (in its algorithmic usage) for

Such understanding of precedents samples allows assert, that any person is solving continuously tasks, programming them in a natural language because the human life is based on precedents. Any person has an experience of programming in a natural language in its algorithmic usage. Let's name such possibility of programming as "a natural programming of a human" (N-programming). Any human has a personal ability of the Nprogramming the experience of which depends on a set of precedents which have been

embedded in the activity in accordance with its essence.

the task aimed at the creation of the definite activity unit.

**1. Introduction** 

(Precedent, 2011).

essence of the human activity."

mastered by the person in the own life.


## **Question-Answer Shell for Personal Expert Systems**

Petr Sosnin

*Ulyanovsk State Technical University, Russia* 

## **1. Introduction**

50 Expert Systems for Human, Materials and Automation

A. Lenarcik and Z. Piasta, "Probabilistic rough classifiers with mixture of discrete and

S. Pal and P. Mitra, "Case generation using rough sets with fuzzy representation", *IEEE Trans. Knowledge and Data Engineering*, vol.16, no.3, pp.292-300, 2004. Z. Pawlak, "Rough sets", *Int. Journal of Information and Computer Science*, vol.11, no.5, pp.341-

Dhawan, A. K. and Kaur,K. (2009). Artificial Intelligence and Fuzzy Expert Systems.

Z. Pawlak, J. Grzymala-Busse, R. Slowinski, and W. Ziarko, "Rough sets", *Communications of* 

J. Peters, D. Lockery, and S. Ramanna, "Monte Carlo off-policy reinforcement learning; A rough set approach", *Proc. 5th Int. Conf. Hybrid Intelligent Systems*, pp.187-192, 2005. F. Radermacher, "Decision support systems: Scope and potential", *Decision Support Systems*,

A. Skowron and C. Rauszer, "The discernibility matrices and functions in information

R. Swiniarski, "Rough sets and principal component analysis and their applications in

L. Yang and L. Yang, "Study of a cluster algorithm based on rough sets theory", *Proc. 6th Int.* 

W. Ziarko, "The discovery, analysis, and representation of data dependencies in databases",

Shapiro, Stuart C. (1992), "Artificial Intelligence", in Shapiro, Stuart C., Encyclopedia of

Simon, H. A. (1965), The Shape of Automation for Men and Management, New York:

H.R. Berenji, Fuzzy logic controller, in: R.R. Yager and L.A. Zadeh, Eds. An Prognostic

D.G. Burkhardt and P.P. Bonissone, Automated fuzzy knowledge base generation and tuning, IEEE Internut.Conf: on Fuzzy Systems (San Diego, 1992) 179-l 88. Graham and P.L. Jones, Expert Systems Knowledge, Uncertainty and Decision (Chapman

K. Hattori and Y. Tor, Effective algorithms for the nearest neighbor method in the clustering

*Conf. Intelligent Systems Design and Applications*, pp.492-496, 2006.

http://www.cse.buffalo.edu/~shapiro/Papers/ai.ps .

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*(AICI'09).* Shanghai, China: IEEE-CS.

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AAAI Press, pp.195-209, 1991.

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problem. Pattern Recognition 26 (1993) 741-746.

Springer, 1998.

Harper & Row

356, 1982.

continuous variables", In T.Y. Lin and N. Cercone (eds.), *Rough Sets and Data Mining: Analysis for Imprecise Data*, Kluwer Academic Publishers, pp.373-383, 1997. D. Miao and L. Hou, "A comparison of rough set methods and representative inductive learning algorithms", *Fundamenta Informaticae*, vol.59, pp.203-218, 2004. P. Pattaraintakorn, N. Cercone, and K. Naruedomkul, "Hybrid intelligent systems: Selecting

attributes for soft computing analysis", *Proc.29th Int.Conf. Computer Software and* 

*International Conference on Artificial Intelligence and Computational Intelligence* 

systems", In R. Slowinski (ed.), *Intelligent Decision Support, Handbook of Applications and advances of the Rough Set Theory*, Kluwer Academic Publishers, pp.331-362, 1992.

feature extraction and selection, data model building and classification", In S. Pal and A. Skowron (eds.), *Fuzzy Sets, Rough Sets and Decision Making Processes*,

In G. Piatetsky-Shapiro and W.J. Frawley (eds.), *Knowledge Discovery in Databases*,

Artificial Intelligence (2nd ed.), New York: John Wiley, pp. 54–57,

modeling. Introduction to Fuzzy Logic Applications in Intelligent Systems (Kluwer

In the near future a ubiquitous computerization of all spheres of the modern human activity, including various forms of the collective activity, will lead to conditions of a life when all population of the Earth will be involved in interactions with computers. Therefore, in usages of computers by the person it is necessary to aspire to a naturalness of such attitudes. The naturalness should be achieved in that sense that any usage of a computer should be embedded in the activity in accordance with its essence.

Any activity is a naturally-artificial process created on the base of a definite set of precedents the samples of which are extracted from the appropriate experience and its models. Such role of precedents is explained with the help of the following definition: "précédents are actions or decisions that have already happened in the past and which can be referred to and justified as an example that can be followed when the similar situation arises" (Precedent, 2011).

Accessible samples of precedents are necessary means for the activity but in a general case such means can be insufficiently. If absent means will be found and the necessary activity will be created then the new sample of precedent can be built for the reuse of this activity. Hence, told above entitles to assert that "the creation and reuse of precedents defines the essence of the human activity."

Each unit of the fulfilled activity must be modeled by the useful way, be investigated and be coded for its reuse as the precedent. In the life all these actions are similar to creating the programs for the building of which a natural language in its algorithmic usage is applied. Moreover such programs as behavioral schemes are built for tasks which have been solved for already created units of the activity. So, any sample of the precedent can be understood as a program which is coded previously at the natural language (in its algorithmic usage) for the task aimed at the creation of the definite activity unit.

Such understanding of precedents samples allows assert, that any person is solving continuously tasks, programming them in a natural language because the human life is based on precedents. Any person has an experience of programming in a natural language in its algorithmic usage. Let's name such possibility of programming as "a natural programming of a human" (N-programming). Any human has a personal ability of the Nprogramming the experience of which depends on a set of precedents which have been mastered by the person in the own life.

Question-Answer Shell for Personal Expert Systems 53

**while** [logical formulae (LF) for motives **M ={Mk}**]

if [LF for precondition U'= {U'n} ],

**as** [ LF for aims C = **{Cl}** ]

 ----------------------------------- there are alternatives **{Pj(rp)}**.

This framework is a human-oriented scheme the human interaction with which activates the internal logical process on the level of the second signal system in human brains. Such logical processes have a dialog nature and for keeping the naturalness the interaction

 **then** [plan of reaction (program) **rq**], **end so** [LF for postconditions **U" = {U"m}**]

The logical framework is used in ESP for creating the precedents models and keeping them in the knowledge base. This fact can be used for indicating the difference between the suggested ESP and known types of ES. It also distinguishes ESP from systems which use case based reasoning (CBR). Measured similarity between cases and the access to them in the form of "cases recognition" are the other differences between CBR-systems

Let's notice that any ES is a kind of rules-based systems any of which are "software systems that applies the rules and knowledge defined by experts in a particular field to a user's data to solve a problem". Any precedent model can be understood as a rule for its owner and it opens the possibility to define the class of personal expert systems. The shell which is described below helps humans in the creation of expert systems belonged to this

There are three ways for the appearance of the precedent sample. The first way is connected with the intellectual processing of the definite behavior which was happened in the past but was estimated by the human as a potential precedent for its reuse in the future. The second way is the creation of the precedent sample in parallel with the its first performance and the third way is an extraction of the precedent model from another's experience and its models. In any of these cases if the precedent sample is being created as fitting the logical framework and filling it by the appropriate content then the human should solve the retrieval and

Named tasks of the retrieval and extraction should be solved in conditions of the chosen framework and the usage of diverse informational sources including different kinds of texts and reasoning. In the solving of this task the important role is intended for the mental reasoning. Taken into account all told above the question-answering has been chosen by author for retrieval and extraction of informational elements needed in the creation of precedents samples. Question-Answering (or shortly QA) is a type of "an information retrieval in which a direct answer is expected in response to a submitted query, rather than a

There were many different QA-methods and QA-systems which have been suggested, investigated and developed in practice of the informational retrieval and extraction (Hirschman, 2001). Possible ways in the evolution of this subject area were marked in the

**2.2 Question-answering in creation and usage of precedents samples** 

extraction tasks of the necessary information from useful sources.

set of references that may contain the answers"(Question, 2011) .

processes outside brains should keep the dialog form also.

**Name of precedent Pi:** 

**c h o i c e** 

and ESP.

class.

One can count any human as an expert who owns the valuable information about personal precedents. Such information can be extracted from the human by the same human and can be used for creating the knowledge base of an expert system built by the human for the own usage. In the described case one can speak about the definite type of expert systems which will be named below as personal expert systems (or shortly be denoted as ESP).

The definite ESP should be created by the person who fulfills roles of the expert, developer and user of such computer assistant. Such type of expert systems should have the knowledge base containing the accumulated personal experience based on precedents. To create the own personal expert system the human should be provided simple, effective and powerful instrumental means. The Question-Answer shell (QA-shell) which is described in this chapter is a system of such means. QA-shell is built on the base of the instrumental system WIQA (Working In Questions and Answers) previously developed for conceptual designing of software intensive systems.

A very important specificity of QA-shell and ESP is a pseudo-programming (Pprogramming) which is used for the creation of precedents samples and also for the work with them in the real time. The language LPP of the P-programming is similar to the natural language in its algorithmic usage. Therefore the P-programming is similar to the N-programming and such similarity essentially simplifies its application in the creation of precedents samples and their use. This specificity takes into account the ordinary human who have decided to use the computer for solving own tasks based on precedents.

The next important specificity is connected with executors of P-programs. There was a time when computers have not been existed and when N-programs of precedents were being executed by certain persons (by intellectual processors or shortly by I-processors). Computer programs (or shortly K-programs) are being executed by computer processors (or shortly K-processors). Any P-program in the ESP is being executed by I-processor and K-processor collaboratively.

The last important specificity is the "material" which is used by the human for writing data and operators of the P-programs on its "surface". This "material" consists of visualized forms for data originally intended for modeling questions and answers in processes of problem-solving. The initial orientation and features of such type of data are being inherited by data and operators of P-programs and for this reason they are declared as P-programs of the QA-type. In further text the abbreviation of QA will use frequently to emphasize the importance of question(s) and answer(s) for the construction(s) labeled by QA.

## **2. Question-answering and programming in subject area of expert systems**

## **2.1 Logical framework for precedent model**

The use of the precedent as a basic unit of the human interaction with own surrounding demands to choose or build adequate patterns for precedents representations. Appropriate patterns should provide the intellectual mastering of precedents and their natural using by the ordinary person.

In accordance with the author opinion the necessary model for the definite precedent can be created on the base of the following logical framework:

One can count any human as an expert who owns the valuable information about personal precedents. Such information can be extracted from the human by the same human and can be used for creating the knowledge base of an expert system built by the human for the own usage. In the described case one can speak about the definite type of expert systems which will be named below as personal expert systems (or shortly be denoted as

The definite ESP should be created by the person who fulfills roles of the expert, developer and user of such computer assistant. Such type of expert systems should have the knowledge base containing the accumulated personal experience based on precedents. To create the own personal expert system the human should be provided simple, effective and powerful instrumental means. The Question-Answer shell (QA-shell) which is described in this chapter is a system of such means. QA-shell is built on the base of the instrumental system WIQA (Working In Questions and Answers) previously developed

A very important specificity of QA-shell and ESP is a pseudo-programming (Pprogramming) which is used for the creation of precedents samples and also for the work with them in the real time. The language LPP of the P-programming is similar to the natural language in its algorithmic usage. Therefore the P-programming is similar to the N-programming and such similarity essentially simplifies its application in the creation of precedents samples and their use. This specificity takes into account the ordinary human

The next important specificity is connected with executors of P-programs. There was a time when computers have not been existed and when N-programs of precedents were being executed by certain persons (by intellectual processors or shortly by I-processors). Computer programs (or shortly K-programs) are being executed by computer processors (or shortly K-processors). Any P-program in the ESP is being executed by I-processor and

The last important specificity is the "material" which is used by the human for writing data and operators of the P-programs on its "surface". This "material" consists of visualized forms for data originally intended for modeling questions and answers in processes of problem-solving. The initial orientation and features of such type of data are being inherited by data and operators of P-programs and for this reason they are declared as P-programs of the QA-type. In further text the abbreviation of QA will use frequently to emphasize the importance of question(s) and answer(s) for the construction(s) labeled

**2. Question-answering and programming in subject area of expert systems** 

The use of the precedent as a basic unit of the human interaction with own surrounding demands to choose or build adequate patterns for precedents representations. Appropriate patterns should provide the intellectual mastering of precedents and their natural using by

In accordance with the author opinion the necessary model for the definite precedent can be

who have decided to use the computer for solving own tasks based on precedents.

for conceptual designing of software intensive systems.

K-processor collaboratively.

**2.1 Logical framework for precedent model** 

created on the base of the following logical framework:

by QA.

the ordinary person.

ESP).

**Name of precedent Pi: while** [logical formulae (LF) for motives **M ={Mk}**] **as** [ LF for aims C = **{Cl}** ] if [LF for precondition U'= {U'n} ], **then** [plan of reaction (program) **rq**], **end so** [LF for postconditions **U" = {U"m}**] ----------------------------------- there are alternatives **{Pj(rp)}**. **c h o i c e** 

This framework is a human-oriented scheme the human interaction with which activates the internal logical process on the level of the second signal system in human brains. Such logical processes have a dialog nature and for keeping the naturalness the interaction processes outside brains should keep the dialog form also.

The logical framework is used in ESP for creating the precedents models and keeping them in the knowledge base. This fact can be used for indicating the difference between the suggested ESP and known types of ES. It also distinguishes ESP from systems which use case based reasoning (CBR). Measured similarity between cases and the access to them in the form of "cases recognition" are the other differences between CBR-systems and ESP.

Let's notice that any ES is a kind of rules-based systems any of which are "software systems that applies the rules and knowledge defined by experts in a particular field to a user's data to solve a problem". Any precedent model can be understood as a rule for its owner and it opens the possibility to define the class of personal expert systems. The shell which is described below helps humans in the creation of expert systems belonged to this class.

## **2.2 Question-answering in creation and usage of precedents samples**

There are three ways for the appearance of the precedent sample. The first way is connected with the intellectual processing of the definite behavior which was happened in the past but was estimated by the human as a potential precedent for its reuse in the future. The second way is the creation of the precedent sample in parallel with the its first performance and the third way is an extraction of the precedent model from another's experience and its models. In any of these cases if the precedent sample is being created as fitting the logical framework and filling it by the appropriate content then the human should solve the retrieval and extraction tasks of the necessary information from useful sources.

Named tasks of the retrieval and extraction should be solved in conditions of the chosen framework and the usage of diverse informational sources including different kinds of texts and reasoning. In the solving of this task the important role is intended for the mental reasoning. Taken into account all told above the question-answering has been chosen by author for retrieval and extraction of informational elements needed in the creation of precedents samples. Question-Answering (or shortly QA) is a type of "an information retrieval in which a direct answer is expected in response to a submitted query, rather than a set of references that may contain the answers"(Question, 2011) .

There were many different QA-methods and QA-systems which have been suggested, investigated and developed in practice of the informational retrieval and extraction (Hirschman, 2001). Possible ways in the evolution of this subject area were marked in the

Question-Answer Shell for Personal Expert Systems 55

MH-processor is defined (Card, 1983) as a system of specialized processors which solve the common task collaboratively. One of these processors is a cognitive processor providing mental reasoning the basic form of which is an implicit dialog (question-answer reasoning, QA-reasoning). Let's count that I-processor is similar to MH-processor and includes the

It is easy to agree that for saving the naturalness the implicit QA-reasoning as a natural form of the cognitive processes inside I-processor should "be translated" and transferred to Kprocessor as an obvious QA-reasoning. Hence, K-processor should include the embedded QA-processor supporting the work with obvious QA-reasoning (or the work with question and answers). Such combining of processors provide their natural coordination in the

Combining of processors is schematically presented in Fig. 1 which is inherited and adapted from Fig. 1 of the ACM SIGCHI Circulium for Human-Computer Interaction

computer human

In scheme the question is understood by the author as the natural phenomenon which appears at the definite situation when the human interacts with the own experience (own precedents). In this case the "question" is a symbolic (sign) model of the appropriate question. Used understanding helps to explain the necessity of fitting the "question" in QAprocesses. Implicit questions and answers exist in reality while "questions" and "answers"

The system named WIQA has been developed previously as QA-processor for the conceptual designing of the Software Intensive System (SIS) by the method of conceptual

In most general case the application of a method begins with the first step of QAanalyzing the initial statement of a development task Z\*(t0). In special cases of its application the initial statement of a task is included in a task tree corresponded to the design technology with which it will be used. The dynamics of the method is presented

"questions

"answers" QA- processor

**2.4 Co-ordination of I-processor and K-processor** 

cognitive component with its named natural functions.

collaborative work managed by the human reasoning.

questions

I-processor

Fig. 1. General question-answer scheme of CHI

**3. QA-processor and its applications 3.1 Conceptual solution of project tasks** 

present them as sign models.

solving the project tasks.

schematically in Fig.2.

answers

(Hewett, 2002).

Roadmap Research (Burger, 2001) which is actual in nowadays. This research has defined the system of concepts, classifications and basic tasks of this subject area.

Applying concepts of the Roadmap Research we can assert that QA-means which are necessary for working with precedents samples should provide the use of "interactive QA" and "advanced reasoning for QA" (Question, 2011). In interactive QA "the questioner might want not only to reformulate the question, but (s)he might want to have a dialogue with the system". The advanced reasoning is used by questioner who "expects answers which are outside the scope of written texts or structured databases" (Question, 2011). Let's remind, that one of informational sources for the creation of precedents samples is mental reasoning in dialog forms.

QA-means are effective and handy instruments not only for the creation of the precedents samples but for their use also. Sequences of questions and answers which had been used in the creation stage of the precedent can be used for the choice of the necessary precedent sample.

## **2.3 Programming in the work with precedents samples**

The important component of logical framework is a reaction plan of the human behavior which should be coded in the precedent sample for the future reuse. Before the appearance of computers and frequently nowadays the ordinary human used and uses the textual forms for registering plans of reactions. If the plan includes conditions and-or cycles then, its text is better to write in pseudo-code language similar to the natural language in its algorithmic use. In this case the reaction plan will have the form of P-program.

The reaction plan in the form of P-program is being created as a technique for solving the major task of the corresponding precedent. The other important task is connected with the search of the suitable sample including its choice in a set of alternatives.

In ESP both of these tasks should be solved and P-programmed by the human for their reuse in the future with the help of computer by the same human. Hence, a set of effective and handy means should be included to ESP for writing and fulfilling QA-programs supporting the work of the human with precedents samples.

There is a feature of P-programs oriented on the work of the human with precedents and their samples. As told above any P-program in ESP is being executed by I-processor and K-processor collaboratively where the role of I-processor is fulfilled by the human. The idea of the human model as I-processor is inherited by the author from a set of publications (Card, 1983; Crystal, 2004) where described the model human processor (MH-processor) as an engineering model of the human performance in solving the different tasks in real time.

The known application of the MH-processor is Executive Process-Interactive Control (EPIC) described detailly in (Kieras, 1997). Means of EPIC support the programming of the human interaction with the computerized system in the specialized pseudo-language Keystrok Level Model (KLM). A set of basic KLM actions includes the following operators: **K -** key press and release (keyboard),**P -** Point the mouse to an object on screen, **B -** button press or release (mouse), **H -** hand from keyboard to mouse or vice versa and others commands. Means of I-processor should support QA-interactions of the human with the precedent reuse process. The major part of such interactions consists of the execution of P-programs embedded to the current precedent sample. The main executor of P-programs is the human who fulfills the role of I-processor.

Roadmap Research (Burger, 2001) which is actual in nowadays. This research has defined

Applying concepts of the Roadmap Research we can assert that QA-means which are necessary for working with precedents samples should provide the use of "interactive QA" and "advanced reasoning for QA" (Question, 2011). In interactive QA "the questioner might want not only to reformulate the question, but (s)he might want to have a dialogue with the system". The advanced reasoning is used by questioner who "expects answers which are outside the scope of written texts or structured databases" (Question, 2011). Let's remind, that one of informational sources for the creation of precedents samples is mental reasoning

QA-means are effective and handy instruments not only for the creation of the precedents samples but for their use also. Sequences of questions and answers which had been used in the creation stage of the precedent can be used for the choice of the necessary precedent

The important component of logical framework is a reaction plan of the human behavior which should be coded in the precedent sample for the future reuse. Before the appearance of computers and frequently nowadays the ordinary human used and uses the textual forms for registering plans of reactions. If the plan includes conditions and-or cycles then, its text is better to write in pseudo-code language similar to the natural language in its algorithmic

The reaction plan in the form of P-program is being created as a technique for solving the major task of the corresponding precedent. The other important task is connected with the

In ESP both of these tasks should be solved and P-programmed by the human for their reuse in the future with the help of computer by the same human. Hence, a set of effective and handy means should be included to ESP for writing and fulfilling QA-programs supporting

There is a feature of P-programs oriented on the work of the human with precedents and their samples. As told above any P-program in ESP is being executed by I-processor and K-processor collaboratively where the role of I-processor is fulfilled by the human. The idea of the human model as I-processor is inherited by the author from a set of publications (Card, 1983; Crystal, 2004) where described the model human processor (MH-processor) as an engineering model of the human performance in solving the

The known application of the MH-processor is Executive Process-Interactive Control (EPIC) described detailly in (Kieras, 1997). Means of EPIC support the programming of the human interaction with the computerized system in the specialized pseudo-language Keystrok Level Model (KLM). A set of basic KLM actions includes the following operators: **K -** key press and release (keyboard),**P -** Point the mouse to an object on screen, **B -** button press or release (mouse), **H -** hand from keyboard to mouse or vice versa and others commands. Means of I-processor should support QA-interactions of the human with the precedent reuse process. The major part of such interactions consists of the execution of P-programs embedded to the current precedent sample. The main executor of P-programs is the human

the system of concepts, classifications and basic tasks of this subject area.

**2.3 Programming in the work with precedents samples** 

the work of the human with precedents samples.

different tasks in real time.

who fulfills the role of I-processor.

use. In this case the reaction plan will have the form of P-program.

search of the suitable sample including its choice in a set of alternatives.

in dialog forms.

sample.

## **2.4 Co-ordination of I-processor and K-processor**

MH-processor is defined (Card, 1983) as a system of specialized processors which solve the common task collaboratively. One of these processors is a cognitive processor providing mental reasoning the basic form of which is an implicit dialog (question-answer reasoning, QA-reasoning). Let's count that I-processor is similar to MH-processor and includes the cognitive component with its named natural functions.

It is easy to agree that for saving the naturalness the implicit QA-reasoning as a natural form of the cognitive processes inside I-processor should "be translated" and transferred to Kprocessor as an obvious QA-reasoning. Hence, K-processor should include the embedded QA-processor supporting the work with obvious QA-reasoning (or the work with question and answers). Such combining of processors provide their natural coordination in the collaborative work managed by the human reasoning.

Combining of processors is schematically presented in Fig. 1 which is inherited and adapted from Fig. 1 of the ACM SIGCHI Circulium for Human-Computer Interaction (Hewett, 2002).

Fig. 1. General question-answer scheme of CHI

In scheme the question is understood by the author as the natural phenomenon which appears at the definite situation when the human interacts with the own experience (own precedents). In this case the "question" is a symbolic (sign) model of the appropriate question. Used understanding helps to explain the necessity of fitting the "question" in QAprocesses. Implicit questions and answers exist in reality while "questions" and "answers" present them as sign models.

## **3. QA-processor and its applications**

## **3.1 Conceptual solution of project tasks**

The system named WIQA has been developed previously as QA-processor for the conceptual designing of the Software Intensive System (SIS) by the method of conceptual solving the project tasks.

In most general case the application of a method begins with the first step of QAanalyzing the initial statement of a development task Z\*(t0). In special cases of its application the initial statement of a task is included in a task tree corresponded to the design technology with which it will be used. The dynamics of the method is presented schematically in Fig.2.

Question-Answer Shell for Personal Expert Systems 57

Z11 Z12

Z1m

Z2

Zp

Z1

Z\*(t)

Z2n Z22 Z21

The conceptual solution is estimated as the completed decision if its state is sufficient for the successful work at the subsequent development stages of SIS. The degree of the sufficiency is obviously and implicitly checked. Useful changes are being added for achieving the more

Zp1

Zp2

Zpr Iterative process

Tasks distribution in designers group

Stepwise refinement

+

+

+

QA-analysis and modeling

Thus, the conceptual solution of the main project task is defined as a system of conceptual diagrams with their accompanied descriptions at the concept language the content of which are sufficient for successful coding of the task solution. Which conceptual diagrams are

As a related works which are touched QA-reasoning, we can mention the reasoning in the "inquiry cycle" (Potts, 1994) for working with requirements, "inquiry wheel" (Reiff, 2002) for scientific decisions and "inquiry map" (Rosen, 2008) used for the education aims. Similar ideas are used in the special question-answer system which supports the development of SIS (Henninger, 2003). The typical schemes of reasoning for SIS development are presented in (Bass, 2005), in (Yang, 2003) reasoning is presented on seven levels of its application together with the used knowledge and in (Lee, 2000) model-based reasoning is presented as useful

The conceptual solution of any project task is based on QA-analysis and QA-modeling. QAanalysis provides the extraction of questions from the task statement and searching and formulating the answers on them. QA-modeling helps to combine questions and answers in QA-model of the task and its parts and for checking them on the correctness and conformity.

included to the solution depends on the technology used for developing the SIS.

Fig. 3. Task tree of development process

adequate conceptual representation of SIS.

means for the software engineering.

**3.2 Question-answering in WIQA** 

Fig. 2. Dynamics of conceptual solving the project task

The system of tasks of conceptual designing the SIS is being formed and solved according to a method of the stepwise refinement. The initial state of the stepwise refinement is defined by the system of normative tasks of the life cycle of SIS which includes the main project task Z\*(t0). The base version of normative tasks corresponds to standard ISO/IEC 12207.

The realization of the method begins with the formulation of the main task statement in the form which allows starting the creation of the prime conceptual models. The initial statement of the main task formulates as the text Z\*(t0) which reflects the essence of the created SIS without details. Details of SIS are being formed with the help of QA-analysis of Z\*(t0) which evolves the informational content of the designing and includes subordinated project tasks (Z1(t1), …, ZI,k(tn), …, ZJ,r(tm)) in the decision of the main task.

The detailed elaboration of SIS forms the system of tasks which includes not only the project tasks connected with the specificity of SIS, but also service tasks, each of which is aimed at the creation of the corresponding conceptual diagram or document. The solutions of project and service tasks are chosen from libraries of normative conceptual models {Mk} and service QA-techniques {QA(Mki)}.

During conceptual decision of any task (included in a tasks tree of the SIS project) additional tasks can be discovered and included to the system of tasks as it shown in Fig. 3. The tasks tree is a dynamic system which is evolved iteratively by the group of designers. The stepwise refinement is used by any designer who fulfils QA-analysis and QA-modeling of the each solved task. General conceptual decision integrates all conceptual decision of all tasks included in a tasks tree of the project.

conceptual project …

The system of tasks of conceptual designing the SIS is being formed and solved according to a method of the stepwise refinement. The initial state of the stepwise refinement is defined by the system of normative tasks of the life cycle of SIS which includes the main project task

Libraru of models {MKj}

Q11 Q12 Q1m Qp1 Q2n Q22 Q21 Q2 Q1 A1 A11 A12 A1m A21 A22 A2n Ap1 Ap2 Apr

A2 Qp Ap Qp2 Qpr Analysis Transformation Representations Visualization **Figure 2.** Logical view

Result of decision =

Q

The realization of the method begins with the formulation of the main task statement in the form which allows starting the creation of the prime conceptual models. The initial statement of the main task formulates as the text Z\*(t0) which reflects the essence of the created SIS without details. Details of SIS are being formed with the help of QA-analysis of Z\*(t0) which evolves the informational content of the designing and includes subordinated

The detailed elaboration of SIS forms the system of tasks which includes not only the project tasks connected with the specificity of SIS, but also service tasks, each of which is aimed at the creation of the corresponding conceptual diagram or document. The solutions of project and service tasks are chosen from libraries of normative conceptual models {Mk} and service

During conceptual decision of any task (included in a tasks tree of the SIS project) additional tasks can be discovered and included to the system of tasks as it shown in Fig. 3. The tasks tree is a dynamic system which is evolved iteratively by the group of designers. The stepwise refinement is used by any designer who fulfils QA-analysis and QA-modeling of the each solved task. General conceptual decision integrates all conceptual decision of all tasks

Z\*(t0). The base version of normative tasks corresponds to standard ISO/IEC 12207.

Z11 Z12 Z1m

Zp1 Z2n Z22 Z21 Z2 Z1 Z

Zp Zp2 Zpr

Decision process

project tasks (Z1(t1), …, ZI,k(tn), …, ZJ,r(tm)) in the decision of the main task.

Library of models {QA(MKj)}

Fig. 2. Dynamics of conceptual solving the project task

Initial statement of Z\*(t0)

Z1(t1)

ZI.k(tn) …

ZJ.r(tm)

QA-techniques {QA(Mki)}.

included in a tasks tree of the project.

#### Fig. 3. Task tree of development process

The conceptual solution is estimated as the completed decision if its state is sufficient for the successful work at the subsequent development stages of SIS. The degree of the sufficiency is obviously and implicitly checked. Useful changes are being added for achieving the more adequate conceptual representation of SIS.

Thus, the conceptual solution of the main project task is defined as a system of conceptual diagrams with their accompanied descriptions at the concept language the content of which are sufficient for successful coding of the task solution. Which conceptual diagrams are included to the solution depends on the technology used for developing the SIS.

As a related works which are touched QA-reasoning, we can mention the reasoning in the "inquiry cycle" (Potts, 1994) for working with requirements, "inquiry wheel" (Reiff, 2002) for scientific decisions and "inquiry map" (Rosen, 2008) used for the education aims. Similar ideas are used in the special question-answer system which supports the development of SIS (Henninger, 2003). The typical schemes of reasoning for SIS development are presented in (Bass, 2005), in (Yang, 2003) reasoning is presented on seven levels of its application together with the used knowledge and in (Lee, 2000) model-based reasoning is presented as useful means for the software engineering.

#### **3.2 Question-answering in WIQA**

The conceptual solution of any project task is based on QA-analysis and QA-modeling. QAanalysis provides the extraction of questions from the task statement and searching and formulating the answers on them. QA-modeling helps to combine questions and answers in QA-model of the task and its parts and for checking them on the correctness and conformity.

Question-Answer Shell for Personal Expert Systems 59

**?… ?…**

**?…** 

A11 A12 A1m A21 A22 A2n

Ap1 Ap2 Apr

**?… ?…** 

**?…** S({Ai})

**?…**

Design process

QA model views

Q11 Q12 Q1m

A2

Q1 A1

Qp1

Qp2 Qpr

Any label has a unique code which includes a capital letter (Z, Q, A, or other) and its index appointed automatically. Any capital letter is presented by the icon and indicates the type or subtype of the visualized object. In WIQA there are means for creating the new icons. The content of such interactive objects are not limited only their textual and graphical expressions which are accessible to the designer via the main interface form. Other "sides" of any QA-model and any interactive object of Z- or Q- or A-type are accessible via plug-ins

QA processor WIQA has been implemented in several versions. Elaborations of two last versions were based on architectural views of QA-model and the usage of repository, MVC, client-server and interpreter architectural styles. Moreover in created versions have been used object-oriented, component-oriented and service-oriented architectural paradigms. One of the last versions named as NetWIQA has been programmed on Delphi 6.0 and the second version (named as WIQA.Net) has been created on C# at the platform of

The structure of WIQA, its functional possibilities and positive effects are described in a set of publications of the author. The features of WIQA are reflected by its general components structure presented in Fig 7 on the background of QA-model to emphasize that components

Q2n Q22 Q21

Qp Ap

Views

**?… ?… ?…** 

Q2

**?…**

**?…**

Q

**?…** 

**?…** 

**?…** 

Fig. 5. QA-model of the task

Fig. 6. QA-protocol of QA-model

**3.3 Applications of WIQA** 

are working with the common QA-database.

Microsoft.Net 3.5.

of WIQA.

Named QA-actions are fulfilled by designer who translates internal QA-reasoning and registers them in QA-database of WIQA. All these works are implemented with using the visual forms presented in Fig. 4. This form fulfils the role of an inter-mediator between Iprocessor and QA-processor. The language of WIQA is Russian therefore fields of the screenshot are marked by labels.

Fig. 4. The main form of QA-processor.

The responsibility for evolving the tasks tree, defining tasks statements and building for them adequate QA-models is laid on designers. For this work they use any informational sources not only mental reasoning. One of these sources is a current content of tasks tree and the current state of QA-model for each task. Therefore a set of commands are accessible to designers for interactions with tasks, questions and answers which are visualized in the main form. The additional commands are accessible via plug-ins of WIQA.

The usage of QA-model of task is a specificity of WIQA as a Question-Answering system. Any QA-model is being formed as an example of QA-sample which is defined as a set of architectural views on the materialization of the model. This set includes, for example, the task view, logical-linguistic view, ontological view and views of other types each of which is being opened for designers with the help of specialized plug-ins.

Question-answer models, as well as any other models, are created "for extraction of answers to the questions enclosed in the model". Moreover, the model is a very important form of representation of questions, answers on which are generated during the interaction with the model. Any designer can get any programmed positive effect with the help of the access to the "answer" on the chosen question actually or potentially included in the appropriate view of QA-model (Fig. 5).

The definite set of questions and answers are available to the designer via visual "side" of QA-model named as QA-protocol the structure of which is presented in Fig. 6.

The field of QA-protocol is marked in the screenshot presented above. The designer can use any visual task for the access to the corresponding QA-protocol. Further the designer can use any question Qi or answer Aj for the access to the content of the corresponding QAmodel. One can interprets labels of Z-, Q- and A-elements at the main interface form as visual addresses of corresponding Z-, Q- and A-objects.

Fig. 5. QA-model of the task

Named QA-actions are fulfilled by designer who translates internal QA-reasoning and registers them in QA-database of WIQA. All these works are implemented with using the visual forms presented in Fig. 4. This form fulfils the role of an inter-mediator between Iprocessor and QA-processor. The language of WIQA is Russian therefore fields of the

QA-protocol

The responsibility for evolving the tasks tree, defining tasks statements and building for them adequate QA-models is laid on designers. For this work they use any informational sources not only mental reasoning. One of these sources is a current content of tasks tree and the current state of QA-model for each task. Therefore a set of commands are accessible to designers for interactions with tasks, questions and answers which are visualized in the

(can be edited) Person responsibility

QA-protocol Picture

Plug-ins

The usage of QA-model of task is a specificity of WIQA as a Question-Answering system. Any QA-model is being formed as an example of QA-sample which is defined as a set of architectural views on the materialization of the model. This set includes, for example, the task view, logical-linguistic view, ontological view and views of other types each of which is

Question-answer models, as well as any other models, are created "for extraction of answers to the questions enclosed in the model". Moreover, the model is a very important form of representation of questions, answers on which are generated during the interaction with the model. Any designer can get any programmed positive effect with the help of the access to the "answer" on the chosen question actually or potentially included in the appropriate

The definite set of questions and answers are available to the designer via visual "side" of

The field of QA-protocol is marked in the screenshot presented above. The designer can use any visual task for the access to the corresponding QA-protocol. Further the designer can use any question Qi or answer Aj for the access to the content of the corresponding QAmodel. One can interprets labels of Z-, Q- and A-elements at the main interface form as

QA-model named as QA-protocol the structure of which is presented in Fig. 6.

main form. The additional commands are accessible via plug-ins of WIQA.

being opened for designers with the help of specialized plug-ins.

visual addresses of corresponding Z-, Q- and A-objects.

screenshot are marked by labels.

Fig. 4. The main form of QA-processor.

Text expression

Other

Task tree

view of QA-model (Fig. 5).

Fig. 6. QA-protocol of QA-model

Any label has a unique code which includes a capital letter (Z, Q, A, or other) and its index appointed automatically. Any capital letter is presented by the icon and indicates the type or subtype of the visualized object. In WIQA there are means for creating the new icons. The content of such interactive objects are not limited only their textual and graphical expressions which are accessible to the designer via the main interface form. Other "sides" of any QA-model and any interactive object of Z- or Q- or A-type are accessible via plug-ins of WIQA.

## **3.3 Applications of WIQA**

QA processor WIQA has been implemented in several versions. Elaborations of two last versions were based on architectural views of QA-model and the usage of repository, MVC, client-server and interpreter architectural styles. Moreover in created versions have been used object-oriented, component-oriented and service-oriented architectural paradigms. One of the last versions named as NetWIQA has been programmed on Delphi 6.0 and the second version (named as WIQA.Net) has been created on C# at the platform of Microsoft.Net 3.5.

The structure of WIQA, its functional possibilities and positive effects are described in a set of publications of the author. The features of WIQA are reflected by its general components structure presented in Fig 7 on the background of QA-model to emphasize that components are working with the common QA-database.

Question-Answer Shell for Personal Expert Systems 61

Q11 Q12 Q1m Qp1 Q21 Q22 Q2n

QUESTION-ASNSWER ENVIRONMENT of WIQA

A11 A12 A1m A21 A22 A2n Ap1 Ap2 Apr

A2

Q11 Q12 Q1m Qp1 Q21 Q22 Q2n

A2 Ap

A11 A12 A1m A21 A22 A2n Ap1 Ap2 Apr

Q11 Q12 Q1m Qp1 Q21 Q22 Q2n

First all works named above have been fulfilled for the specialized ES with knowledge base oriented on its filling by samples of precedents extracted from international rules for collision avoidance at sea (COLREG-72) (Cockcroft, 2003). After that the work was repeated creatively and QA-shell for ESP has been elaborated. Thus the elaboration of the own ESP is

The usage of Question-Answering is the main specificity of both elaborations which opens for the human the right QA-access not only to the knowledge base (precedents base). The human has the direct access to any task of the tasks tree of ES or ESP and therefore to any QA-protocol or QA-model in any its state. The human can use such uniform access for the analysis of solution processes in any interval of time and for modeling the evolving the

The creation of the new precedent sample Pi is a specially important for the human who elaborates and uses the own ESP. Such creation is being implemented technologically as the elaboration of SIS also but SIS of the precedent type. This point of view opens the possibility

PT PL PG PG PI PE

for registering a set of elaboration states in life cycle of precedent (Fig. 9)

System of operations

 **while** [logica formulae (F) for motives **M ={Mk}]**

 if [F for precondition **U'= {U'n}** ],  **then** [plan of reaction (program) **rq]**,  **end so** [F for postconditions **U" = {U"m}]**

A11 A12 A1m A21 A22 A2n Ap1 Ap2 Apr

A11 A12 A1m A21 A22 A2n Ap1 Ap2 Apr

Qp

Forming the knowledge base

Substantiation

Zp

Qp Ap

Qp Ap

Qp Ap

Interpreter Knowledge

Qp2 Qpr

Qp2 Qpr

Zp2 Zpr

base

Qp2 Qpr

life cycle

Qp2 Qpr

Z11 Z1m Z12 Z21 Z22 Z2n Zp1

A2

Z2

Q1 Q2

A2

Q11 Q12 Q1m Qp1 Q21 Q22 Q2n

Q1 Q2

A1

Q1 Q2

Working area

Q

Q1 Q2

implemented as creating the SIS of the ESP type.

**4.2 Composite structure of precedent samples** 

 **Name of precedent Pi:** 

**c h o i c e** 

**as** [ F for aims **C = {Cl}** ]

 ----------------------------------- there are alternatives **{Pj(rp)}**.

Fig. 9. Presentations of precedent models on the line of its life cycle

Q

Q

A1

A1

Z1

Interface

A1

Fig. 8. Emulation of ES in WIQA

Z\*

Q

events in ES or ESP.

Fig. 7. Components structure of WIQA

As told above WIQA has been created for designing the SIS. The practice of this activity has shown that WIQA can be used as a shell for the creation of some applications. By present time on the basis of this shell, for example, the following applications have been elaborated: DocWIQA for the creation and manage of *living documents*, EduWIQA for the automated teaching, TechWIQA for technological preparation for production and EmWIQA for the expert monitorng of the sea vessel surrounding.

The last application of WIQA is QA-shell for personal expert systems which is being described in this chapter. This QA-shell inherits basic means of WIQA and evolves them by necessary plug-ins supporting the activity based on precedents. Some inheritances were described above and consequently some features of ESP are already presented.

## **4. Elaboration of expert system on the base of WIQA**

## **4.1 Question-answer modeling the basic tasks of expert system**

The description of ESP will be continued in the form of its elaboration in WIQA with the inheritance basic means of WIQA, and also their necessary modifying and evolving. First question is about QA-modeling the typical tasks of ES without their orientation to ESP. The answer this question is connected with immersing the ES into WIQA which is schematically presented in Fig. 8.

The "Block and line" view in Fig 8 is chosen specially, so that it corresponds to the typical scheme of the ES. The structure of the ES is presented on the background of QA-model and also as early for emphasizing the functional style of immersing the ES to its model of QA-type.

The corresponding task should be defined and programmed for each block of ES in its chosen immersing. The tasks structure and the definition of each necessary task can be presented in WIQA in the form of the tasks tree. Each task of this tree can be solved conceptually by the step-wise refinement method. After that each built solution should be distributed between Iprocessor and QA-processor and necessary computer components should be programmed. In such approach to the elaboration of ES one can assert that possibilities of WIQA means are used for the emulation of ES in WIQA as into the instrumental shell.

Zp

Simulator of expert system

Qp

Library of patterns

Qp2 Qpr

Qp2 Qpr

Qp

Qp

Means of evolving (components, data, agents)

Ap

Ap

Ap

Zp2 Zpr

Qp2 Qpr

Qp2 Qpr

Z1m Z11 Z12 <sup>Z</sup> Zp1 Z21 Z22 2n

Editors: text &graphics

Web-shell Orgstructure

Q11 Q12 Q1m Qp1 Q21 Q22 Q2n

A11 A12 A1m A21 A22 A2n Ap1 Ap2 Apr

A2

Plug-ins of Application

As told above WIQA has been created for designing the SIS. The practice of this activity has shown that WIQA can be used as a shell for the creation of some applications. By present time on the basis of this shell, for example, the following applications have been elaborated: DocWIQA for the creation and manage of *living documents*, EduWIQA for the automated teaching, TechWIQA for technological preparation for production and EmWIQA for the

The last application of WIQA is QA-shell for personal expert systems which is being described in this chapter. This QA-shell inherits basic means of WIQA and evolves them by necessary plug-ins supporting the activity based on precedents. Some inheritances were

The description of ESP will be continued in the form of its elaboration in WIQA with the inheritance basic means of WIQA, and also their necessary modifying and evolving. First question is about QA-modeling the typical tasks of ES without their orientation to ESP. The answer this question is connected with immersing the ES into WIQA which is schematically

The "Block and line" view in Fig 8 is chosen specially, so that it corresponds to the typical scheme of the ES. The structure of the ES is presented on the background of QA-model and also as early for emphasizing the functional style of immersing the ES to its model of QA-type. The corresponding task should be defined and programmed for each block of ES in its chosen immersing. The tasks structure and the definition of each necessary task can be presented in WIQA in the form of the tasks tree. Each task of this tree can be solved conceptually by the step-wise refinement method. After that each built solution should be distributed between Iprocessor and QA-processor and necessary computer components should be programmed. In such approach to the elaboration of ES one can assert that possibilities of WIQA means are

described above and consequently some features of ESP are already presented.

**4. Elaboration of expert system on the base of WIQA** 

**4.1 Question-answer modeling the basic tasks of expert system** 

used for the emulation of ES in WIQA as into the instrumental shell.

Q11 Q12 Q1m Qp1 Q21 Q22 Q2n

A2 Ap

Q21 Q22 Q2n

A2

Q11 Q12 Q1m Qp1 Q21 Q22 Q2n

Base of Precedents

A11 A12 A1m A21 A22 A2n Ap1 Ap2 Apr

A11 A12 A1m A21 A22 A2n Ap1 Ap2 Apr

Qp

Z2

Basic components of WIQA

Q1 Q2

QA-protocols

A2

Q11 Q12 Q1m Qp1

A11 A12 A1m A21 A22 A2n Ap1 Ap2 AprQ

Q1 Q2

A1

Interpreter pseudocodes

Visualization means

Q1 Q2

Q

QA-database

Q

Q1 Q2

Q

Fig. 7. Components structure of WIQA

A1

A1

expert monitorng of the sea vessel surrounding.

Task tree

Z1

A1

presented in Fig. 8.

Z\*

Fig. 8. Emulation of ES in WIQA

First all works named above have been fulfilled for the specialized ES with knowledge base oriented on its filling by samples of precedents extracted from international rules for collision avoidance at sea (COLREG-72) (Cockcroft, 2003). After that the work was repeated creatively and QA-shell for ESP has been elaborated. Thus the elaboration of the own ESP is implemented as creating the SIS of the ESP type.

The usage of Question-Answering is the main specificity of both elaborations which opens for the human the right QA-access not only to the knowledge base (precedents base). The human has the direct access to any task of the tasks tree of ES or ESP and therefore to any QA-protocol or QA-model in any its state. The human can use such uniform access for the analysis of solution processes in any interval of time and for modeling the evolving the events in ES or ESP.

#### **4.2 Composite structure of precedent samples**

The creation of the new precedent sample Pi is a specially important for the human who elaborates and uses the own ESP. Such creation is being implemented technologically as the elaboration of SIS also but SIS of the precedent type. This point of view opens the possibility for registering a set of elaboration states in life cycle of precedent (Fig. 9)

Fig. 9. Presentations of precedent models on the line of its life cycle

Question-Answer Shell for Personal Expert Systems 63

The ordinary person in own ESP should have the possibility for programming the behavior embedded to the precedent sample. As told above the best way for fulfilling such work is the use of P-programming which is supported by handy automated means included to

Any P-program is better for understanding as the code of interactions of the person with the corresponding precedent. In WIQA the normative way for interactions is QA-reasoning. Hence is better to adapt the means of QA-reasoning for their use in P-programming. For such adaptation it is necessary to find the ways for emulations (wuith the help of QA-

Expressions of data and operators of P-programs by means of QA-reasoning is only one part of QA-approach to P-programming. This part should be expanded by the interpreter which transforms any written P-programs in collaborative actions of the person and computer. Both named parts of QA-approach to P-programming are defined and implemented with their orientation on the ordinary person. To distinguish P-programs of such type from other

The type of QA-data has been defined for expressions of data and operators by means of QA- reasoning. Features of this type D will be opened on the example of its simple subtype which consists of a "question" Qi and appropriate "answer" Ai which haven't the subordinated "questions" and "answers". In this case the "name" and "value" of the definite data Di are written in attributes of Qi and Ai which are intended for the textual expression of Qi and Ai in QA-database. All other attributes Qi and Ai are inherited by Di . The attributes structure of Di is presented in Fig.12 where not only attributes of QA-database are indicated but additional attributes which are defined by the user also. In general case QA-data are an association of simple data each of which is based on the corresponding pair

Di

Means of additional attributes (AA) are embedded to WIQA for simplifying the elaboration of new plug-ins. The mechanism of AA implements the function of the object-relational mapping of QA-data to programs objects with planned characteristics. One version of such objects is classes in C#. The other version is fitted for pseudo-code programming. The scheme which is used in WIQA for the object-relational mapping is presented in Fig. 13. The usage of the AA is supported by the specialized plug-ins embedded in WIQA. This plug-ins helps the user to declare the necessary attribute or a group of attributes for definite Z-, Q- and A-elements. In any time the user can view declared attributes for the chosen element. Other actions with the AA must be programmed in C# or in the pseudo-code

Textual expression Ai

Additional attributes of user

Other attributes of Ai in QA-database

reasoning) data and operators of the appropriate language of P-programming.

**5. Pseudo-programming in WIQA 5.1 QA-approach to P-programming** 

P-programs they have been named QA-programs.

Textual expression Qi

Fig. 12. Attributes stricture of the simple QA-data

Additional attributes of user

WIQA.

of Qi and Ai.

Other attributes of Qi in QA-database

language supported by WIQA.

This set includes the following useful precedent models: PT - textual precedent description, PL - logical (predicate) model, PG - graphical (diagrammatic) model, PQA - question-answer model, PI - source program code and PE - executed code. All of these models are included to the typical materialization of the precedent sample in the knowledge base (precedets base).

The composite structure of the precedent sample and the specificity of its production units were chosen for their usage by I-processor firstly and for the usage by K-processor secondly. The first version of the typical precedent sample which was used for coding the rules of COLREG'72 is presented in Fig. 10. This version is included to QA-shell of ESP

Fig. 10. Structure of the typical precedent sample in the knowledge base of EmWIQA

Precedents used in EmWIQA are accessible as for the user (sailor on duty) so for software agents which are presenting the vessels in the definite sea area. The usage of the automatic access of the vessel agent to the precedents sample in EmWIQA has led the author to the second version of precedents samples which uses P-programming for the work with conditions and reactions in samples of precedents in the form of software agents (Fig. 11).

Fig. 11. Precedent sample as a sotware agent

In the second version any precedent sample is presented as an autonomous software unit the access to which is being processed in accordance with conditions of the precedent usage. It is supposed that conditions are defined and described by the person (human) in text form in the natural language (from this point of text we will use the word "person" instead the word "human" to emphasize the context of the personal expert system).

The input text is being processed step by step by a set of input units (morfologic analyzer, ontological filter,key words filter, compiler of condition). If the precedent sample has been chosen and the corresponding precedent has been fulfilled then a set of output units can be activate automated by the person and automatically for registering post-conditions (events on blackboard, output data). The second version is included to QA-shell of ESP partially.

## **5. Pseudo-programming in WIQA**

62 Expert Systems for Human, Materials and Automation

This set includes the following useful precedent models: PT - textual precedent description, PL - logical (predicate) model, PG - graphical (diagrammatic) model, PQA - question-answer

**PT**

**PQA** 

**P<sup>L</sup>**

**P<sup>G</sup>**

**PI** 

**P<sup>E</sup>**

the typical materialization of the precedent sample in the knowledge base (precedets base). The composite structure of the precedent sample and the specificity of its production units were chosen for their usage by I-processor firstly and for the usage by K-processor secondly. The first version of the typical precedent sample which was used for coding the rules of

COLREG'72 is presented in Fig. 10. This version is included to QA-shell of ESP

V

Fig. 10. Structure of the typical precedent sample in the knowledge base of EmWIQA

Precedents used in EmWIQA are accessible as for the user (sailor on duty) so for software agents which are presenting the vessels in the definite sea area. The usage of the automatic access of the vessel agent to the precedents sample in EmWIQA has led the author to the second version of precedents samples which uses P-programming for the work with conditions and reactions in samples of precedents in the form of software agents (Fig. 11).

Input\_Units\_1 Input\_Unit\_2 Input\_Unit\_N Output\_Unit\_1 Output\_Unit\_2 Output\_Unit\_M

In the second version any precedent sample is presented as an autonomous software unit the access to which is being processed in accordance with conditions of the precedent usage. It is supposed that conditions are defined and described by the person (human) in text form in the natural language (from this point of text we will use the word "person"

The input text is being processed step by step by a set of input units (morfologic analyzer, ontological filter,key words filter, compiler of condition). If the precedent sample has been chosen and the corresponding precedent has been fulfilled then a set of output units can be activate automated by the person and automatically for registering post-conditions (events on blackboard, output data). The second version is included to QA-shell of ESP partially.

instead the word "human" to emphasize the context of the personal expert system).

**P<sup>G</sup>**

**PI** 

**P<sup>E</sup>**

**PQA P<sup>L</sup>**

**PT**

precedent sample Pi

Keys

Name

Rating

Software agent (precedent sample Pi)

Fig. 11. Precedent sample as a sotware agent

V


model, PI

## **5.1 QA-approach to P-programming**

The ordinary person in own ESP should have the possibility for programming the behavior embedded to the precedent sample. As told above the best way for fulfilling such work is the use of P-programming which is supported by handy automated means included to WIQA.

Any P-program is better for understanding as the code of interactions of the person with the corresponding precedent. In WIQA the normative way for interactions is QA-reasoning. Hence is better to adapt the means of QA-reasoning for their use in P-programming. For such adaptation it is necessary to find the ways for emulations (wuith the help of QAreasoning) data and operators of the appropriate language of P-programming.

Expressions of data and operators of P-programs by means of QA-reasoning is only one part of QA-approach to P-programming. This part should be expanded by the interpreter which transforms any written P-programs in collaborative actions of the person and computer.

 Both named parts of QA-approach to P-programming are defined and implemented with their orientation on the ordinary person. To distinguish P-programs of such type from other P-programs they have been named QA-programs.

The type of QA-data has been defined for expressions of data and operators by means of QA- reasoning. Features of this type D will be opened on the example of its simple subtype which consists of a "question" Qi and appropriate "answer" Ai which haven't the subordinated "questions" and "answers". In this case the "name" and "value" of the definite data Di are written in attributes of Qi and Ai which are intended for the textual expression of Qi and Ai in QA-database. All other attributes Qi and Ai are inherited by Di .

The attributes structure of Di is presented in Fig.12 where not only attributes of QA-database are indicated but additional attributes which are defined by the user also. In general case QA-data are an association of simple data each of which is based on the corresponding pair of Qi and Ai.

Fig. 12. Attributes stricture of the simple QA-data

Means of additional attributes (AA) are embedded to WIQA for simplifying the elaboration of new plug-ins. The mechanism of AA implements the function of the object-relational mapping of QA-data to programs objects with planned characteristics. One version of such objects is classes in C#. The other version is fitted for pseudo-code programming. The scheme which is used in WIQA for the object-relational mapping is presented in Fig. 13. The usage of the AA is supported by the specialized plug-ins embedded in WIQA. This plug-ins helps the user to declare the necessary attribute or a group of attributes for definite Z-, Q- and A-elements. In any time the user can view declared attributes for the chosen element. Other actions with the AA must be programmed in C# or in the pseudo-code language supported by WIQA.

Question-Answer Shell for Personal Expert Systems 65

*Name Description .................... Value* 

Necessary methods (operations)

As told above there is a pssibility to create and use the icon for the necessary types or subtypes for Z-, Q- and A-objects. QA-variables can be qualified as a definite type of Q- and A-objects. For this type the icons for letters D and V instead of icons for letters Q and A are

An example of keeping the array with elements of the integer type is presented in Fig. 8 where a set of additional attributes are used for translating the array declaration to

Attributes which are assigned for the array are visually accessible for the person at any time and can be used not only for translating. The person can add useful attributes to the set of array attributes for example for describing its semantic features which will be checked in

Let's open some features of additional attributes for data declarations. For the chosen Qelement the person can appoint not only the definite attribute AAm but the type Tk of AAm with characteristics of type Tk and also a set of subordinated attributes {AAmn} with the

The other useful AAi

Additional attributes

Attribute Value Type\_data Array Measure 1 Type\_element integer Number 5

*Attributes declared by user* 

> *Type of variable, Attributes of type*

*Additional attributes* 

QA-variable

Basic attributes of QA-data

Index(Address) «Creator» Time of changes ..................... Type of visual icon

QA-protocol D1. Array & Name & D1.1. Name[0] V1.1. 12 D1.1. Name[0]

V1.1. 5

V1.1. 0

Fig. 15. Declaration of array

creating and executing QA-program.

D1.1. Name[0] V1.1. -7 D1.1. Name[0]

D1.1. Name[0] V1.1. 22

Fig. 14. Imitation of variable

created and used.

computer codes.

Fig. 13. Creation of additional attributes

Thus in Di the field for the textual expression of Qi can be used for writing the declaration of the necessary element of data or operator of P-program. In this case the corresponding field for the textual expression of Ai will be used for coding the "value" of data or the result of the operator execution.

Hence, any line of any P-program is possible to write on the "surface" of the corresponding Q-element which can be interpreted as a "material for writing" with useful properties. This "material" consists of visualized forms for writing the string of symbols. The initial orientation and features of such type of strings are being inherited by data and operators of P-programs and for this reason they are declared as P-programs of QA-type. In order to separate this type of P-programs from P-programs of the others types, they will be named as QA-programs. Such name of P-programs is rightful as the pseudo-code text of any line can be qualified as a "question" on which the interpreter of QA-program builds the corresponding "answer".

## **5.2 Emulation of pseudo-code data**

There are two types of lines of the source pseudo-code one of which intends for the data emulation and another for the operator emulation. Let's begin to describe the emulation of QA-data.

First of all the AA-mechanism was used for the creation a subset of objects imitated the typical data (such as scalars of traditional types, array, record, set and list) in the forms of packed classes (Fig. 14).

For the declaration of variables the constructor of QA-data has been developed. This constructor gives the possibilities to name QA-variable, to choose its type and to appoint the initial value of the variable. The constructor can be used as the self-dependent utility or can be embedded to the translator of pseudo-programs which is implemented as a compiler and an interpreter (in two versions).

Let's remember that any unit of QA-data is created for its use by I-processor firstly and for the computer processor secondly. The visualized declaration of QA-data of the necessary type and the touchable appointment of the necessary visual value take into account the interactions possibilities of I-processor. But any declared QA-variable is accessible automatically for the appropriate programs executed by the computer processor also.

Relation on QAdatabase

Access to QA-data

Fig. 13. Creation of additional attributes

the operator execution.

corresponding "answer".

packed classes (Fig. 14).

an interpreter (in two versions).

QA-data.

**5.2 Emulation of pseudo-code data** 

Mechanisms of AA

Relations of AA-plug-ins

A set of classes (additional attributes)

Thus in Di the field for the textual expression of Qi can be used for writing the declaration of the necessary element of data or operator of P-program. In this case the corresponding field for the textual expression of Ai will be used for coding the "value" of data or the result of

Hence, any line of any P-program is possible to write on the "surface" of the corresponding Q-element which can be interpreted as a "material for writing" with useful properties. This "material" consists of visualized forms for writing the string of symbols. The initial orientation and features of such type of strings are being inherited by data and operators of P-programs and for this reason they are declared as P-programs of QA-type. In order to separate this type of P-programs from P-programs of the others types, they will be named as QA-programs. Such name of P-programs is rightful as the pseudo-code text of any line can be qualified as a "question" on which the interpreter of QA-program builds the

There are two types of lines of the source pseudo-code one of which intends for the data emulation and another for the operator emulation. Let's begin to describe the emulation of

First of all the AA-mechanism was used for the creation a subset of objects imitated the typical data (such as scalars of traditional types, array, record, set and list) in the forms of

For the declaration of variables the constructor of QA-data has been developed. This constructor gives the possibilities to name QA-variable, to choose its type and to appoint the initial value of the variable. The constructor can be used as the self-dependent utility or can be embedded to the translator of pseudo-programs which is implemented as a compiler and

Let's remember that any unit of QA-data is created for its use by I-processor firstly and for the computer processor secondly. The visualized declaration of QA-data of the necessary type and the touchable appointment of the necessary visual value take into account the interactions possibilities of I-processor. But any declared QA-variable is accessible automatically for the appropriate programs executed by the computer processor also.

User or the new function for automatic use

Virtual relation (additional attributes)

server

client

Fig. 14. Imitation of variable

As told above there is a pssibility to create and use the icon for the necessary types or subtypes for Z-, Q- and A-objects. QA-variables can be qualified as a definite type of Q- and A-objects. For this type the icons for letters D and V instead of icons for letters Q and A are created and used.

An example of keeping the array with elements of the integer type is presented in Fig. 8 where a set of additional attributes are used for translating the array declaration to computer codes.

Fig. 15. Declaration of array

Attributes which are assigned for the array are visually accessible for the person at any time and can be used not only for translating. The person can add useful attributes to the set of array attributes for example for describing its semantic features which will be checked in creating and executing QA-program.

Let's open some features of additional attributes for data declarations. For the chosen Qelement the person can appoint not only the definite attribute AAm but the type Tk of AAm with characteristics of type Tk and also a set of subordinated attributes {AAmn} with the

Question-Answer Shell for Personal Expert Systems 67

and labeled subtypes of QA-operators. The person can appoint additional attributes for any QA-operator and such attributes can be used obviously in the text of QA-program, for

Any QA-program creates for the division of the problem-solving process among the person an computer. In this case the division is presented in the form of the source pseudo-code the interactions with which are used as the person so the computer. The definite task of human-

 But interactions on the base of QA-programs have the additional features. These features are implemented in interactions of persons with Z-, Q- and A-objects which are used for registering the lines of pseudo-code source of QA-programs. As told above such interactive

Both named features define the essence of QA-programming for I-processors firstly and for computer processors secondly. The basic aim of the interaction is the access to the person

The structure of any precedent includes a condition part and a part of a reaction each of which should be QA-programmed. The value "truth" in the estimation of the conditional part opens the access to the execution of the appropriate reaction. Therefore QA-programs for estimating the conditions of precedents and QA-programs for executing the reaction part

But as told above, some QA-programs can be written for their translating and executing as computer programs. Some of such QA-programs can be created for supporting the work with "precedents" in the definite application. The system of QA-programs was created by

QA-programs, which are oriented on the computer execution, are useful in cases when the direct access to the visualized data is profitable for example for developers of SISs or for their users (documenting, decision-making, expert estimating and other tasks). Such programs are suitable when the library of QA-templates (not precedents samples) can be created for a set of typical tasks solving in SISs. The possibility of working with QA-

For the real time working of I-processor with precedents the following QA-program scheme

QA-PROGRAM\_1(condition for the access to the precedent):

experience in the precedents forms for its inclusion to the problem-solving processes.

example, for operations with comments included to QA-program lines.

computer interactions can be solved with the help of its QA-programming.

**6. Specimens of QA-programs** 

objects open very useful positive effects for persons.

of precedents are two basic types of QA-programs.

author for the collision avoidance expert system of the sea vessel.

templates and the library of templates are included to WIQA.

D1. Variable V\_1 / Comment\_1?

D2. Variable V\_2 / Comment\_2?

…………………………………………… DN. Variable V\_M / Comment\_M?

OJ. F = Logical expression (V\_1, V\_2, …, V\_M)?

V1.Value of V\_1.

V2. Value of V\_2.

VN. Value of V\_M.

End.

AJ. Value of Expression.

**6.1 Types of QA-programs** 

is useful:

appropriate type Tn for each of which. All these attributes and types with their values can be used by the person in the creation of QA-programs. Such possibilities help the person in Pprogramming the work with semantics of QA-variables. The named effects can be used in Pprogramming the planned or real time work with pseudo-code operators also.

## **5.3 Emulation of pseudo-code operators**

The second type of pseudo-code lines are intended for writing the operators. As it was for QA-data we can define for operators the next interpretations:


In other words, the string of symbols for the "question" can be used for writing (in this place) the operator in the pseudo-code form. The fact or the result of the operator execution will be marked or registered in the string of the symbol for the "answer". Such version of emulating the operator has been named as QA-operator. The expression of any QA-operator can be understood as the "question" about the action which is coded. The execution af QAoperator builds the "answer" this "question".

The next step in the emulation of operators is connected with taking into account types of operators. For simulating the basic pseudo-program operators the next constructions were chosen:


In named operators the following definitions of functions and procedures are used:


The set of basic operators includes traditional pseudo-code operators but each of which inherits the feature of the appropriate QA-unit also. Hence, the basic attributes of QA-unit and necessary additional attributes can be taken into account in processing the operator and not only in its translation. In order to underline the specificity of operators emulation they will be indicated as QA-operators.

In pseudo-programming languages a set of basic operators is being expanded usually. In the described case the expansion includes cycle-operators such as «**for**», "**while-do**" and «**dountil**». Emulations of QA-data and QA-operators are implemented in WIQA and provide the creation of pseudo-code programs for different tasks.

As for QA-variables the special icons for letters "O" (for operator) and "E" (is executed) have been created and used instead icons for letters "Q" and "A". The person can defined and labeled subtypes of QA-operators. The person can appoint additional attributes for any QA-operator and such attributes can be used obviously in the text of QA-program, for example, for operations with comments included to QA-program lines.

## **6. Specimens of QA-programs**

## **6.1 Types of QA-programs**

66 Expert Systems for Human, Materials and Automation

appropriate type Tn for each of which. All these attributes and types with their values can be used by the person in the creation of QA-programs. Such possibilities help the person in Pprogramming the work with semantics of QA-variables. The named effects can be used in P-

The second type of pseudo-code lines are intended for writing the operators. As it was for

• "answer" indicates by the special marker about "the fact that the operator was

In other words, the string of symbols for the "question" can be used for writing (in this place) the operator in the pseudo-code form. The fact or the result of the operator execution will be marked or registered in the string of the symbol for the "answer". Such version of emulating the operator has been named as QA-operator. The expression of any QA-operator can be understood as the "question" about the action which is coded. The execution af QA-

The next step in the emulation of operators is connected with taking into account types of operators. For simulating the basic pseudo-program operators the next constructions were

• **Goto:**"question" → "condition" and "answer" → "go to the definite operator of QA-

• **Function**: "question" → "definition of function" and "answer" → "compute the value"; • **Procedure**: "question" → "definition of procedure" and "answer" → "execute the

• **End**: "question" → "end of program" and "answer" → "finish the work with QA-

• any procedure is a typical sequence of actions which are accessible in QA-processor for

The set of basic operators includes traditional pseudo-code operators but each of which inherits the feature of the appropriate QA-unit also. Hence, the basic attributes of QA-unit and necessary additional attributes can be taken into account in processing the operator and not only in its translation. In order to underline the specificity of operators emulation they

In pseudo-programming languages a set of basic operators is being expanded usually. In the described case the expansion includes cycle-operators such as «**for**», "**while-do**" and «**dountil**». Emulations of QA-data and QA-operators are implemented in WIQA and provide

As for QA-variables the special icons for letters "O" (for operator) and "E" (is executed) have been created and used instead icons for letters "Q" and "A". The person can defined

In named operators the following definitions of functions and procedures are used:

• any function is defined as the expression written in the P-language;

• **Appoint**: "question" → "name of variable" and "answer" → "appoint the value;

• **If: «**question**»** → **«**condition**» Then «**answer**»** → **«Execute** the definite operator»; • **Command**: "question" →" the command of QA-processor" and "answer" → "execute

programming the planned or real time work with pseudo-code operators also.

**5.3 Emulation of pseudo-code operators** 

operator builds the "answer" this "question".

fulfilled".

program;

the command";

procedure".

program".

the execution by the person.

will be indicated as QA-operators.

the creation of pseudo-code programs for different tasks.

chosen:

QA-data we can define for operators the next interpretations: • "question" is " a symbolic presentation of an operator";

Any QA-program creates for the division of the problem-solving process among the person an computer. In this case the division is presented in the form of the source pseudo-code the interactions with which are used as the person so the computer. The definite task of humancomputer interactions can be solved with the help of its QA-programming.

 But interactions on the base of QA-programs have the additional features. These features are implemented in interactions of persons with Z-, Q- and A-objects which are used for registering the lines of pseudo-code source of QA-programs. As told above such interactive objects open very useful positive effects for persons.

Both named features define the essence of QA-programming for I-processors firstly and for computer processors secondly. The basic aim of the interaction is the access to the person experience in the precedents forms for its inclusion to the problem-solving processes.

The structure of any precedent includes a condition part and a part of a reaction each of which should be QA-programmed. The value "truth" in the estimation of the conditional part opens the access to the execution of the appropriate reaction. Therefore QA-programs for estimating the conditions of precedents and QA-programs for executing the reaction part of precedents are two basic types of QA-programs.

But as told above, some QA-programs can be written for their translating and executing as computer programs. Some of such QA-programs can be created for supporting the work with "precedents" in the definite application. The system of QA-programs was created by author for the collision avoidance expert system of the sea vessel.

QA-programs, which are oriented on the computer execution, are useful in cases when the direct access to the visualized data is profitable for example for developers of SISs or for their users (documenting, decision-making, expert estimating and other tasks). Such programs are suitable when the library of QA-templates (not precedents samples) can be created for a set of typical tasks solving in SISs. The possibility of working with QAtemplates and the library of templates are included to WIQA.

For the real time working of I-processor with precedents the following QA-program scheme is useful:

QA-PROGRAM\_1(condition for the access to the precedent):

D1. Variable V\_1 / Comment\_1? V1.Value of V\_1. D2. Variable V\_2 / Comment\_2? V2. Value of V\_2. …………………………………………… DN. Variable V\_M / Comment\_M? VN. Value of V\_M.

OJ. F = Logical expression (V\_1, V\_2, …, V\_M)?

AJ. Value of Expression.

End.

Question-Answer Shell for Personal Expert Systems 69

are kept in the special library. Any QA-program of this library is kept in the special area of QA-database and registered in its catalog which is visually accessible to the person. Let's notice that the greater part of WIQA techniques are being inherited by QA-shell for

If the person needs to use the typical QA-program (needs to solve the typical task with QAmodel implemented as QA-program) the person extracts the typical QA-program from the library, creates the new task, includes the task to the tasks tree and after such actions the

The reality of the person activity is a parallel work with many tasks at the same time. Therefore the special interpreter for executing QA-procedures and the system of interruption are included into WIQA. It gives the possibility to interrupt any QA-procedure (if it is necessary) for working with other QA-programs. The interruption system supports

As told above WIQA was used for elaboration the application EmWIQA provided the expert monitoring of the sea vessel surrounding. This application uses the base of precedents and means of QA-programming. The behavior of users in EmWIQA can be qualified as the potential behaviour of the person in ESP. Therefore QA-programs in

One of such QA-programs is QA-function supports the access to the precedent sample which presents the 15th rule of the International Rules for Preventing Collisions at Sea

O7.CPA = expression for computing the Closest Point of Approach (CPA)?

This QA-function is shown with demonstrated aims only and therefore without explaining the variables and expressions. This function is kept in the knowledge base (with embedded

person can start to solve the task (to execute the corresponding QA-program).

the return to any interrupted QA-program to its point of the interruption.

QA-PROGRAM\_3 (conditional access to the precedent).

D1. Velocity V1 of the power driven vessel V\_1?

D4. Velocity V2 of the power driven vessel V\_2?

O8. Cond = (V\_1, "keep out of the way")& & (│Bear\_1 - Bear\_2│ > 11, 5о) &

E8. Manoeuvre\_Mi / Call of the appropriate QA-procedure.

EmWIQA can be used as examples of QA-programs in ESP.

ESP.

**6.2 Example of QA-functions** 

V1.Value of V1.

V2.Value of B1.

V4.Value of V2.

V5.Value of B2.

E7. Value of CPA.

O9. End.

D2. Bear\_B1 of the vessel V\_1?

D5. Bear\_B2 of the vessel V\_2?

D6. Place of the vessel V\_2? V6. Coordinates of the place\_2.

& (CPA-DDA- ∆D1 ≤ 0)?

D3. Place of the vessel V\_1? V3. Coordinates of the place\_1.

(Cockcroft, 2003):

It is necessary to notice that the person can build or to modify or to fulfill (step by step) the definite example of this program in the real time work with the corresponding precedent which, it may be, the person creates. In presented typical scheme the logical expression is defined for the function F.

The next typical scheme reflects the work with techniques programmed as QAprocedures:

QA-PROGRAM\_2 (technique for the typical task): P1.K\_i, K\_j, …, PL\_k ? E1. \* P2. K\_m, QA-P\_n, …, K\_q? E2.\* ……………………………… PN. K\_s, Pl\_t, …, QA-P\_v? EN. # End.

The program text includes the symbolic names K\_x and Pl-y for the Command and Plug-ins of WIQA and QA-P\_z for QA-program written by means of WIQA. It is necessary to notice that all names of the types K\_x, Pl-y and QA-P\_z are indicated positions on the monitor screen for initiating the actions by touch of the person. In this typical scheme the symbols "\*" and "#" (as "yes" and "no") indicate the facts of the execution for operators.

The following fragment of the Outlook reset actions demonstrates (without E-units) one type of QA-procedures:

P1. Quit all programs.

P2. **Start** On the menu **Run**, click.

P3. **Open** In the box **regedit**, type, and then **OK** the click.

P4. Move to and select the following key:

HKEY\_CURRENT\_USER/Software/Microsoft/Office/9.0/Outlook/

P5. In the Name list, **FirstRunDialog** select.

P6. If you want to enable only the **Welcome to Microsoft Outlook** greeting, on the Edit menu **Modify**, click the type **True** in the Value Data box, and then **OK** the click.

P7. If you also want to re-create all sample welcome items, move to and select the following key:

HKEY\_CURRENT\_USER/Software/Microsoft/Office/9.0/Outlook/Setup

 P8. In the **Name** list, select and delete the following keys: **CreateWelcome First-Run**

P9. In the **Confirm Value Delete** dialog box click **Yes** , for each entry.

P.10. On the **Registry** menu, click **Exit**,.

P11. End.

This type provides the work of the person with service techniquea of the definite application. WIQA and QA-shell are examples of such application. About three hundred typical techniques are implemented as QA-programs for designing the SISs with instruments of WIQA. A half of these QA-programs are the guide type. To remember such (or more) quantity of QA-programs are impossile. Therefore all typical QA-programs

It is necessary to notice that the person can build or to modify or to fulfill (step by step) the definite example of this program in the real time work with the corresponding precedent which, it may be, the person creates. In presented typical scheme the logical expression is

The next typical scheme reflects the work with techniques programmed as QA-

The program text includes the symbolic names K\_x and Pl-y for the Command and Plug-ins of WIQA and QA-P\_z for QA-program written by means of WIQA. It is necessary to notice that all names of the types K\_x, Pl-y and QA-P\_z are indicated positions on the monitor screen for initiating the actions by touch of the person. In this typical scheme the symbols "\*" and "#" (as "yes" and "no") indicate the facts of the

The following fragment of the Outlook reset actions demonstrates (without E-units) one

HKEY\_CURRENT\_USER/Software/Microsoft/Office/9.0/Outlook/

P6. If you want to enable only the **Welcome to Microsoft Outlook** greeting, on the Edit menu **Modify**, click the type **True** in the Value Data box, and then **OK** the

P7. If you also want to re-create all sample welcome items, move to and select the

P8. In the **Name** list, select and delete the following keys: **CreateWelcome First-**

HKEY\_CURRENT\_USER/Software/Microsoft/Office/9.0/Outlook/Setup

This type provides the work of the person with service techniquea of the definite application. WIQA and QA-shell are examples of such application. About three hundred typical techniques are implemented as QA-programs for designing the SISs with instruments of WIQA. A half of these QA-programs are the guide type. To remember such (or more) quantity of QA-programs are impossile. Therefore all typical QA-programs

P9. In the **Confirm Value Delete** dialog box click **Yes** , for each entry.

P3. **Open** In the box **regedit**, type, and then **OK** the click.

QA-PROGRAM\_2 (technique for the typical task):

defined for the function F.

E1. \*

E2.\*

EN. # End.

execution for operators.

type of QA-procedures:

click.

**Run**

P11. End.

following key:

P1. Quit all programs.

P2. **Start** On the menu **Run**, click.

P4. Move to and select the following key:

P.10. On the **Registry** menu, click **Exit**,.

P5. In the Name list, **FirstRunDialog** select.

P1.K\_i, K\_j, …, PL\_k ?

P2. K\_m, QA-P\_n, …, K\_q?

……………………………… PN. K\_s, Pl\_t, …, QA-P\_v?

procedures:

are kept in the special library. Any QA-program of this library is kept in the special area of QA-database and registered in its catalog which is visually accessible to the person. Let's notice that the greater part of WIQA techniques are being inherited by QA-shell for ESP.

If the person needs to use the typical QA-program (needs to solve the typical task with QAmodel implemented as QA-program) the person extracts the typical QA-program from the library, creates the new task, includes the task to the tasks tree and after such actions the person can start to solve the task (to execute the corresponding QA-program).

The reality of the person activity is a parallel work with many tasks at the same time. Therefore the special interpreter for executing QA-procedures and the system of interruption are included into WIQA. It gives the possibility to interrupt any QA-procedure (if it is necessary) for working with other QA-programs. The interruption system supports the return to any interrupted QA-program to its point of the interruption.

## **6.2 Example of QA-functions**

As told above WIQA was used for elaboration the application EmWIQA provided the expert monitoring of the sea vessel surrounding. This application uses the base of precedents and means of QA-programming. The behavior of users in EmWIQA can be qualified as the potential behaviour of the person in ESP. Therefore QA-programs in EmWIQA can be used as examples of QA-programs in ESP.

One of such QA-programs is QA-function supports the access to the precedent sample which presents the 15th rule of the International Rules for Preventing Collisions at Sea (Cockcroft, 2003):

QA-PROGRAM\_3 (conditional access to the precedent).

D1. Velocity V1 of the power driven vessel V\_1?

V1.Value of V1.

D2. Bear\_B1 of the vessel V\_1?

V2.Value of B1.


V5.Value of B2.

D6. Place of the vessel V\_2?


& (CPA-DDA- ∆D1 ≤ 0)?

E8. Manoeuvre\_Mi / Call of the appropriate QA-procedure.

O9. End.

This QA-function is shown with demonstrated aims only and therefore without explaining the variables and expressions. This function is kept in the knowledge base (with embedded

Question-Answer Shell for Personal Expert Systems 71

Interfaces of the main form help to control as executing QA-program so its debugging. The person who is fulfilling the role of I-processor can interrupt I-process on any operator of

In the set of named translators for indicating the types of operators the following variants

In accordance with told above, the usage of the potential of Z-, Q- and A-objects for emulating the typical data and simulating the basic program operators opens the possibility to create QA-programs which can be translated for their executing by computer processors

Pseudo-code texts of QA-programs can be written and executed (in the real time) by the person working in the corporate network. The person interacts with QA-programs as with inter-mediators between the person and computers and it gives the arguments to qualify their as new type of means for human-computer interactions. Moreover, such intermediators can be translated (in WIQA) firstly to the C# source code and then to the executed

The practice of QA-programming has shown that visual forms of WIQA presented in Fig. 4 are unsufficient for the usability of QA-programs created by the person in ESP. Therefore the plug-ins "Generator of interface units" has been created and embedded to

The necessary interface unit is being generated from the drawn interface diagram which is being translated to the scheme of the corresponding QA-program. After that the scheme of

executed operator

dictionary Function

library

QA-program with the possibility of returning to the point of the interruption.

• appointment the type with the help of additional attributes (as for QA-data).

interrupt

• inclusion the key words into the symbolic presentation of operators;

• selection the type of the operator from the emerging menu;

QA-pseudocode

Pascal-like code

has been used and checked:

execute

Fig. 16. Screenshot of interpreter

**7.3 Generator of interface units** 

QA-program is filling by the chosen interfaces precedents.

also.

code.

QA-shell.

precedents) into the EmWIQA and function is accessible for program agents (automatically) and for the sailor on duty (in the automated regime). The knowledge base of the EmWIQA consists of 155 units each of which includes QA-function for choosing the precedent and QA-procedure for its executing.

## **7. Means for development and usage of personal expert systems**

## **7.1 Additional means of WIQA**

As told above AS-shell of ESP inherits the basic means of WIQA presented in Fig. 7. These means include the simulator of expert system elaborated previously for EmWIQA, base of precedents with their coding in the first version and the interpreter which uses the means of the dynamic compilation of Microsoft.Net 3.5. After estimation all of these means from the point of view of ESP the WIQA has been evolved with the orientation on the ordinary person.

The additional technological QA-programs have been added to the specialized system of QA-programs simulating the expert system. The first version of coding the precedent sample is modified by the inclusion to it the possibility of QA-programming the conditional access to the sample (morphologic analysis of key words and compilation of QA-functions). The language of P-programming has been modified by the inclusion to its grammar the description of additional attributes.

Following components have been developed and included in the ES-shell additionally:


## **7.2 Translators of QA-programs**

Translation means for the pseudo-programming are evolved step by step from one kind of QA-programs to the other kind. Two compilers and two interpreters are embedded in QAshell for ESP.

The first compiler provides the processing of QA-programs which describe the conditional parts of precedents. Copies of such compiler can be embedded by the person to the precedent samples implemented as agents. The second compiler supports the translation of QA-programs in the executed codes (.dll-forms).

Both interpreters are intended for I-processors. There are the following differences between interpreters - the first interpreter can work with cycle operators and the second interpreter uses the mechanism of the dynamic compilation for the current line of QA-program which is being executed.

Let's present some details for the first interpreter. As other translators embedded in WIQA this interpreter is worked with the LP-language. The lexicon of the created QA-program can be chosen by the programmer (by the person). For the declaration of QA-data the specialized utility program is developed. This utility program supports the work with data of traditional algorithmic types. The main window of the interpreter is presented in Fig. 16 with commentary labels.

precedents) into the EmWIQA and function is accessible for program agents (automatically) and for the sailor on duty (in the automated regime). The knowledge base of the EmWIQA consists of 155 units each of which includes QA-function for choosing the precedent and

As told above AS-shell of ESP inherits the basic means of WIQA presented in Fig. 7. These means include the simulator of expert system elaborated previously for EmWIQA, base of precedents with their coding in the first version and the interpreter which uses the means of the dynamic compilation of Microsoft.Net 3.5. After estimation all of these means from the point of view of ESP the WIQA has been evolved with the orientation on the ordinary

The additional technological QA-programs have been added to the specialized system of QA-programs simulating the expert system. The first version of coding the precedent sample is modified by the inclusion to it the possibility of QA-programming the conditional access to the sample (morphologic analysis of key words and compilation of QA-functions). The language of P-programming has been modified by the inclusion to its grammar the

Following components have been developed and included in the ES-shell additionally:

• a specialized generator of interface units for helping the person to combine QA-

• a set of means for simplifying the work of the person aimed at the creation of precedent samples, their inclusion to the precedents base, access to the necessary sample and its

Translation means for the pseudo-programming are evolved step by step from one kind of QA-programs to the other kind. Two compilers and two interpreters are embedded in QA-

The first compiler provides the processing of QA-programs which describe the conditional parts of precedents. Copies of such compiler can be embedded by the person to the precedent samples implemented as agents. The second compiler supports the translation of

Both interpreters are intended for I-processors. There are the following differences between interpreters - the first interpreter can work with cycle operators and the second interpreter uses the mechanism of the dynamic compilation for the current line of QA-program which is

Let's present some details for the first interpreter. As other translators embedded in WIQA this interpreter is worked with the LP-language. The lexicon of the created QA-program can be chosen by the programmer (by the person). For the declaration of QA-data the specialized utility program is developed. This utility program supports the work with data of traditional algorithmic types. The main window of the interpreter is presented in Fig. 16

• a set of translators (compilers and interpreters) of QA-programs;

programs and executed codes of other types;

QA-programs in the executed codes (.dll-forms).

**7. Means for development and usage of personal expert systems** 

QA-procedure for its executing.

**7.1 Additional means of WIQA** 

description of additional attributes.

**7.2 Translators of QA-programs** 

person.

use.

shell for ESP.

being executed.

with commentary labels.

Interfaces of the main form help to control as executing QA-program so its debugging. The person who is fulfilling the role of I-processor can interrupt I-process on any operator of QA-program with the possibility of returning to the point of the interruption.

In the set of named translators for indicating the types of operators the following variants has been used and checked:


Fig. 16. Screenshot of interpreter

In accordance with told above, the usage of the potential of Z-, Q- and A-objects for emulating the typical data and simulating the basic program operators opens the possibility to create QA-programs which can be translated for their executing by computer processors also.

Pseudo-code texts of QA-programs can be written and executed (in the real time) by the person working in the corporate network. The person interacts with QA-programs as with inter-mediators between the person and computers and it gives the arguments to qualify their as new type of means for human-computer interactions. Moreover, such intermediators can be translated (in WIQA) firstly to the C# source code and then to the executed code.

## **7.3 Generator of interface units**

The practice of QA-programming has shown that visual forms of WIQA presented in Fig. 4 are unsufficient for the usability of QA-programs created by the person in ESP. Therefore the plug-ins "Generator of interface units" has been created and embedded to QA-shell.

The necessary interface unit is being generated from the drawn interface diagram which is being translated to the scheme of the corresponding QA-program. After that the scheme of QA-program is filling by the chosen interfaces precedents.

Question-Answer Shell for Personal Expert Systems 73

subsystem based on precedents. For example, QA-samples of precedents were embedded in system for Expert Monitoring of Environment of the Sea Vessel. QA-samples of precedents also have been used in the solution of following tasks: Creation of Interface Prototypes in context of ISO standard 9126; Information Safety of SIS in the context of ISO standard 15408;

Bass, L.; Ivers J. & Klein, M. & Merson, P. (2005). *Reasoning Frameworks*, Software

Burger, J. et al. (2001). *Issues, Tasks and Program Structures to Roadmap Research in Question &* 

Card S.K.; Thomas, T.P. & Newell, A. (1983). *The Psychology of Human-Computer Interaction*,

Cockcroft, A.N. (2003). *Guide to the Collision Avoidance Rules: International Regulations for* 

Crystal, A. & Ellington, B. (2004). *Task analysis and human-computer interaction: approaches,* 

Henninger, S. (2003). *Tool Support for Experience-Based Software Development Methodologies*,

Hewett, T.; Baecker , R., Card , St., Carey , T., Gasen , J., Mantei, M., Perlman , G., Strong,

Hirschman, L. & Gaizauskas, R. (2001). *Natural Language Question Answering: The View from* 

Karray, F.; Alemzadeh, M., Saleh, J. A. & Arab, M. N. (2008). *Human-Computer Interaction:* 

Kieras, D. & Meyer , D.E. (1997). *An overview of the EPIC architecture for cognition and* 

Lee, M.H. (2000). *Model-Based Reasoning: A Principled Approach for Software Engineering*,

Potts, C.; Takahashi, A. & Anton, K. (1994) *Inquiry-based Requirements Analysis,* IEEE

Reiff, R.; Harwood, W. & Phillipson, T. *A (*2002*) Scientific Method Based Upon Research* 

*Scientists' Conceptions of Scientific Inquiry,* In Proc.2002 Annual International Conference of the Association for the Education of Teachers in Science, pp 546–556.

*techniques, and levels of analysis.* In proceedings of the Tenth Americas Conference on

G., & Verplank, W. (2002). *ACM SIGCHI Curricula for Human-Computer Interaction.* 

*Overview on State of the Art* Smart sensing and intelligent systems, vol. 1, No.

*performance with application to human-computer interaction.* Human-Computer

*Preventing Collisions at Sea*, Butterworth-Heinemann, 2003.

Information Systems, New York, New York, pp 1-9.

*Here*. Natural Language Engineering, vol. 7, pp. 67-87.

Software - Concepts and Tools, vol.19, #4, pp. 179-189.

http://dictionary.reference.com/browse/precedent.

http://www.wordiq.com/definition/Question\_ answering.

Advances in Computers, vol. 59, pp. 29-82.

ACM Technical Report. P. 162.

1(Mar), pp 138-159, 2008.

Interaction, 12, 1997, 391-438.

Software, vol.11, #2, pp. 21-32.

Precedent. Available from

Question-Answering. Available from

Engineering Institute, Carnegie Mellon University, Pittsburgh, PA, Tech. Rep.

Predicative Ontological Testing of Project Solutions.

*Answering (Q&A)*, Tech. Rep. NIST.

London: Lawrence Erbaum Associates.

CMU/SEI-2005-TR-007.

**9. References** 

Any interface precedent is coded the corresponding metrics of usability. A set of usability metrics includes a subset of metrics which are defined in the standard ISO/ MEK–9126. Other metrics were chosen from other useful sources. Any metrics included to the library are defined as an appropriate task which is solved in QA-shell.

## **7.4 Creation and usage of precedent sample**

Any precedent sample is coded as a composite QA-program the integrity of which is provided by its interface shell. The special plug-ins of WIQA which was named "Elaboration of precedent sample" has been created for writing the codes of sample parts and assembling them as a whole. This plug-ins is similar to the elaboration means of traditional programs but it fits on QA-programming.

The graphic editor embedded to plug-ins helps the person to assemble the current sample by filling its typical graphic form which is a copy of scheme presented in Fig. 11. When assembling is finished the precedent sample is uploaded to the corresponding section of QA-program library.

Any precedent sample is an autonomous software unit which is QA-programmed and can be qualified as the software agent. One of the advantages of the agent of such type is the possibility for its easy reprogramming in the real time.

If a number of precedent samples are necessary for the person who are solving the current task they should be extracted from the precedent base (with using the techniques of ESP) and uploaded into the active tasks tree.

## **8. Conclusion**

Told above contains sufficient arguments to assert that the described QA-shell helps to create the Expert Systems of the new type. This type of ES is intended for the ordinary person who has decided to create the ES which will be filled by the valuable information about personal precedents. In creation of own ESP the person fulfills roles of the expert, developer and user of such computer assistant.

The main specificity of the elaborated QA-shell for ESP defines Question Answering which is fitted to pseudo-programming of precedents samples. Accessible means of Question Answering are coordinated with the dialogue nature of consciousness that simplifies transition from internal reasoning of the person to their models in the computer environment. Therefore the owner of ESP can apply real time P-programming of I-processor and K-processor for solving own tasks on the base of precedents the samples of which are kept in ESP.

Accessible means of P-programming is similar to N-programming and their power (types of data, additional attributes and system of P-programming) open the possibility for the ordinary person to write non-trivial programs of the own activity. QA-programs manage accustomed (habitual) semi-automatic actions when QA-programs (as techniques of the guide type) show to the person the sequence of actions which the person must execute. Moreover, QA-programs can be translated in the form which can be executed by the computer processors.

QA-shell is elaborated on the base of the sufficient experince of Question Answering applied to the development of SIS and other applications including applied systems with ES subsystem based on precedents. For example, QA-samples of precedents were embedded in system for Expert Monitoring of Environment of the Sea Vessel. QA-samples of precedents also have been used in the solution of following tasks: Creation of Interface Prototypes in context of ISO standard 9126; Information Safety of SIS in the context of ISO standard 15408; Predicative Ontological Testing of Project Solutions.

## **9. References**

72 Expert Systems for Human, Materials and Automation

Any interface precedent is coded the corresponding metrics of usability. A set of usability metrics includes a subset of metrics which are defined in the standard ISO/ MEK–9126. Other metrics were chosen from other useful sources. Any metrics included to the library

Any precedent sample is coded as a composite QA-program the integrity of which is provided by its interface shell. The special plug-ins of WIQA which was named "Elaboration of precedent sample" has been created for writing the codes of sample parts and assembling them as a whole. This plug-ins is similar to the elaboration means of

The graphic editor embedded to plug-ins helps the person to assemble the current sample by filling its typical graphic form which is a copy of scheme presented in Fig. 11. When assembling is finished the precedent sample is uploaded to the corresponding section of

Any precedent sample is an autonomous software unit which is QA-programmed and can be qualified as the software agent. One of the advantages of the agent of such type is the

If a number of precedent samples are necessary for the person who are solving the current task they should be extracted from the precedent base (with using the techniques of ESP)

Told above contains sufficient arguments to assert that the described QA-shell helps to create the Expert Systems of the new type. This type of ES is intended for the ordinary person who has decided to create the ES which will be filled by the valuable information about personal precedents. In creation of own ESP the person fulfills roles of the expert,

The main specificity of the elaborated QA-shell for ESP defines Question Answering which is fitted to pseudo-programming of precedents samples. Accessible means of Question Answering are coordinated with the dialogue nature of consciousness that simplifies transition from internal reasoning of the person to their models in the computer environment. Therefore the owner of ESP can apply real time P-programming of I-processor and K-processor for solving own tasks on the base of precedents the samples of which are

Accessible means of P-programming is similar to N-programming and their power (types of data, additional attributes and system of P-programming) open the possibility for the ordinary person to write non-trivial programs of the own activity. QA-programs manage accustomed (habitual) semi-automatic actions when QA-programs (as techniques of the guide type) show to the person the sequence of actions which the person must execute. Moreover, QA-programs can be translated in the form which can be executed by the

QA-shell is elaborated on the base of the sufficient experince of Question Answering applied to the development of SIS and other applications including applied systems with ES

are defined as an appropriate task which is solved in QA-shell.

**7.4 Creation and usage of precedent sample** 

traditional programs but it fits on QA-programming.

possibility for its easy reprogramming in the real time.

and uploaded into the active tasks tree.

developer and user of such computer assistant.

QA-program library.

**8. Conclusion** 

kept in ESP.

computer processors.


http://dictionary.reference.com/browse/precedent.

Question-Answering. Available from

http://www.wordiq.com/definition/Question\_ answering.

Reiff, R.; Harwood, W. & Phillipson, T. *A (*2002*) Scientific Method Based Upon Research Scientists' Conceptions of Scientific Inquiry,* In Proc.2002 Annual International Conference of the Association for the Education of Teachers in Science, pp 546–556.

**5** 

*România* 

Zaharia Mihai Horia

**AI Applications in Psychology** 

The AI role in psychology is still underestimated by the European psychology experts. Sometimes psychologists reject the use of expert systems in their fields of activity because they fear that the computer will replace them. Sometimes they do not perceive the full potential of using IT. The same reactions have been encountered among medicine doctors when the first automatic diagnose system was tested. The AI has not reached yet that level of performance capable of emulating simultaneously all pieces of human behaviour, but researchers are on the right track of getting there (Klein, 1999). Anyhow, there are many

One intersection is related to the cognitivist approach in psychology. Within this domain, various programs have been developed for environment simulation, automatic emotion recognition, the simulations of social interaction within groups, phobias therapies, computer aided treatment in psychiatry, electronic inquires and automatic results generation, and the list may continue. In the UK, studies related to the efficiency in applying IT in cognitive behaviour therapy have already been conducted (NICE, 2008) and the results are promising. The importance of IT in psychology was recognised by the researchers' community by

Two distinct levels of IT use in psychotherapy have already been identified (Hovell & Muller, 2010), especially from the patient treatment point of view. Within the first layer, we encounter the common tools developed to increase the efficiency and performance of the therapist. Within the second level, we have the complex systems that help both the patient and the therapist during the treatment. There is a strong possibility that in the future low and medium complexity problems will be handled by the expert systems. Although there are some applications that sustain these assumptions, some controversies on the subject still exist (Marks et al., 2007). In the second part of this chapter, a new approach in information

For the researcher, two information flows are critical. One refers the new discoveries regarding the global research within his area of interest. The other consists of the experimental data needed for his research. Because psychologists measure the thoughts, feelings and behaviour of one or more people at a time, they have a problem in acquiring research data, especially when large numbers of subjects are needed. At a corporate level, this problem is solved by using the electronic version of classical inquires. Though, this solution is limited to a medium where there are strong rules that guide employee behaviour. On the other hand, young people are more and more adapted to the information society. As

**1. Introduction** 

intersection points between these two domains.

developing a new area of research – cyberpsychology.

retrieval and testing will be presented.

*"Gheorge Asachi" Technical University of Iaşi,* 


## **AI Applications in Psychology**

## Zaharia Mihai Horia

*"Gheorge Asachi" Technical University of Iaşi, România* 

## **1. Introduction**

74 Expert Systems for Human, Materials and Automation

Rich, C. & Feldman, Y. (1992). *Seven Layers of Knowledge Representation and Reasoning in* 

Yang, F.; Shen, R. & Han, P. (2003). *Adaptive Question and Answering Engine Base on Case* 
