Preface

In developing nonlinear expert system simulation models, the proper selection of input variables is a challenging problem. Therefore, a false combination of input variables could prevent the simulation model from achieving the optimal solution. The presented methodology in this book is an applicable approach to input variable selection in multi-input simulators of expert systems (ES).

Large-scale ES usually have a hierarchical structure, including personnel and various technical devices, which consume various material, financial, and information resources, as well as energy. As a result, they produce new resources (objects), which are delivered to other similar systems. The main features of ES are large dimensionality and high volatility of their structures, equipment, consumed/ produced objects, and, above all, operation logics and dynamics. In this way, ES are dedicated to a new knowledge representation model, providing convergence of classical operations research and modern knowledge engineering.

As a theoretical approach, this book develops a primary survey of the knowledge representation model, providing convergence of classical operations research and modern knowledge engineering. This convergence creates new opportunities for complicated problems of formalization and solution by integrating the best features of mathematical programming or constraint programming.

This book is a review of classic and Bayesian classification and regression tree approaches with an emphasis on Bayesian approaches, as a first comprehensive review of Bayesian classification and regression trees. Bayesian trees have advantages in comparison to classic tree-based approaches. But Bayesian tree approaches investigate different tree structures with different splitting variables, splitting rules, and tree sizes, so these models can explore the tree space more than classic tree approaches.

Also presented are three semiautomatic methods, found in an exploratory study through a literature review that reduces the burden for experts. These methods help to minimize the effects of human biases by reducing the parameters that are required to construct complete node probability tables. These methods are highly reliable on the input data elicited from experts and present one of many probability elicitation techniques as an example, which can improve the input data needed by the semiautomatic methods and reduce the garbage in/garbage out effect.

The book presents an online system for a 3D representation of programming students' profiles on software metrics that quantify effort and quality of programming from the analysis of source codes. The advantages of this system are enabling the analysis of where the learning difficulties begin, the monitoring of how a class evolves along a course, and informing assessment criteria through the dynamic composition of rubric representations. The system proposed presents itself as a relevant tool to assist teachers regarding decisions of an evaluative process, assisting students from the beginning to the end of a course.

This book has six chapters and explains that expert systems are products in the field of computer science that attempt to perform as intelligent software. What is remarkable for expert systems is the applicability area and the solving of different issues in many fields or industrial branches.

> **Petrică Vizureanu** Gheorghe Asachi Technical University of Iași, Romania

> > **1**

**Chapter 1**

Solution

**1. Introduction**

*Petrică Vizureanu*

programs and applications.

expert leaves or dies [2].

maintain the system.

company (taking his expertise with him) or retire.

Introductory Chapter: Enhanced

What is the definition of an expert system? An expert system belongs to a field of artificial intelligence, and it is a computer program or a software, which can do the same task of a human expert. It can give reliable advice in a specific area of expertise (its domain)and get new conclusions about difficult activities to examine [1]. An expert system can explain its reasoning everytime and is able to interact with a user in the same way that you might consult a human expert. Also, an expert system can be defined as a software program that can outline reasoned conclusions from an amount of knowledge in a specific domain and aims to develop "smart"

The human experts are not 100% reliable in different domains, which can be taken into consideration the advantages and benefits of all accomplished things, but they may disagree with each other or forget to take into account a crucial parameter before making a decision. A human expert can have unsurpassed knowledge in the field and can gain as much knowledge as possible, but be hopeless explaining that to someone else. Human experts cannot be available all the time; i.e., (i) in a small company, the expert on some area can be ill or on holiday; (ii) your doctor may not be able to see you until next week; and (iii) a human expert can move to another

An expert system does not get tired. An expert system (properly programmed)

Problems with expert systems: limited domain; systems are not always up to date and do not learn, has no "common sense" and experts are needed to setup and

Many applications of expert systems are very well known: Prospector—used by geologists to identify sites for drilling or mining; PUFF—medical system for diagnosis of respiratory conditions; Design Advisor—gives advice to designers of processor chips; MYCIN—medical system for diagnosing blood disorders (first used in 1979); LITHIAN—gives advice to archeologists examining stone tools; and DENDRAL—

used to identify the structure of chemical compounds (first used in 1965).

should be 100% reliable and can combine the expertise of several experts. An expert system should be realized to explain and justify all advices it gives. Although an expert system can be expensive to develop, once it is there, its running costs should be low, so there will be economic benefits for the company. An expert system is always available. You can take it with you if you have a notebook or Internet connection, so you could consult an expert system over the Internet. Once the knowledge has been programmed/inserted into the system, it will not be lost if the human

Expert System - A Long-Life

## **Chapter 1**
