**8. Acknowledgments**

I would like to acknowledge my heartfelt and sincere gratitude to my family. A special thanks to all my projects partners and various anonymous reviewers for their valuable advice, insight, comments, and suggestions. This research is funded by a private company in cooperation with Dunărea de Jos University of Galati, Romania, under the research Projects 556/2009 and 557/2010. I am extremely thankful for their financial assistance.

### **9. References**

Alavi, M. (1999), Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues

Barachini, F. (1990), Match-Time Predictability in Real-Time Production Systems, Lecture Notes in Artificial Intelligence, no 462, Expert Systems in Engineering, Principles and Applications, International Workshop, Viena, Austria, September, 1990, Proceedings, Springer-Verlag, p. 190-203, ISBN 3-540-53104-1

ii. the knowledge fuzzy model for the problem presented was developed incrementally, as was embedded in the model sufficient domain knowledge, resulting from the limitations observed in the crisp case. Is it possible to constantly adapt the fuzzy model

iii. the inferential subsystem based on fuzzy logic solves the control situations correctly, both from the computational point of view and in terms of the semantics of the

iv. modeling the process and the expert system as systems with logical events allowed the

v. the control module integrated into the inferential subsystem automatically adapts the problem-solving process, being equivalent to closed-loop system input, denoted UI (user input). In this way, through the control meta-rule 12, we can simulate practically the activation of the relation between output Hypotheses/Conclusions and user inputs. This component is not activated in every case (initial loads). This justifies a major feature of the designed control expert system: its ability to correctly solve the problem, under the conditions specified for each case; vi) The case when ck**N+** is not a particular

*ck* **R+** due to certain restrictions on the events trajectories and to system's

lack of flexibility in achieving the balance, thus leading to partial balance. For the discrete case, there is a tolerated unbalance value, denoted and for which |xi-xj| , which defines the invariant set. For the fuzzy case, is achieved a good balancing solution, regardless of the initial subsystems' loads. In this case the system is broadly

We also summarize possible further development, which inherently can be obtained starting from KMSFL: identifying stronger planning characteristics, the logical specification of the real-time expert systems so that to describe how the statements change their truth values, depending on time or in order to meet some strong real-time restrictions, knowledge acquisition (as a difficult and insufficiently formalized problem), as well as the correct choice of the inference operators that lead to consistent results with regard to the welldefined knowledge semantics, integrating a control expert system like the one developed (agent) into a multi-agent system structure, identifying real problems in different

I would like to acknowledge my heartfelt and sincere gratitude to my family. A special thanks to all my projects partners and various anonymous reviewers for their valuable advice, insight, comments, and suggestions. This research is funded by a private company in cooperation with Dunărea de Jos University of Galati, Romania, under the research Projects 556/2009 and 557/2010. I am extremely thankful for their financial assistance.

Alavi, M. (1999), Knowledge Management and Knowledge Management Systems:

Barachini, F. (1990), Match-Time Predictability in Real-Time Production Systems, Lecture

Proceedings, Springer-Verlag, p. 190-203, ISBN 3-540-53104-1

Notes in Artificial Intelligence, no 462, Expert Systems in Engineering, Principles and Applications, International Workshop, Viena, Austria, September, 1990,

conclusions inferred through the chosen inference scheme;

asymptotically stable relative to event trajectories.

application areas that can be solved using the expert system developed.

Conceptual Foundations and Research Issues

through simulations;

case for

**8. Acknowledgments** 

**9. References** 

qualitative analysis of KMSFL;


**2** 

*Spain* 

**Integration of Knowledge Management in the** 

In a heterogeneous and distributed context, the management of telecommunication networks and services is becoming increasingly important in operator and service provider environments. This management cannot be performed without the contribution of intelligent management functions, which ensure the most important management operations of provisioning, assurance and billing. More advanced tools are needed to support this activity. It is necessary to develop new models, which offer more possibilities. To resolve this difficulty in this chapter we study the integration of advanced artificial intelligence technology into existing network management models. We describe the design and implementation of a management platform using Artificial Intelligent reasoning

This study focuses on an intelligent framework and a language for formalizing knowledge management descriptions and combining them with existing OSI management model. In this chapter we present a methodology to specify intelligent agents, based on management

We propose a new paradigm where the intelligent network management is integrated into the conceptual repository of management information. In modern network elements management information is increasingly stored in a distributed manner locally with the network elements into Management Information Base (MIB) databases. These databases contain all relevant configuration data and the dynamic state data (measurements and alarms) in a standardized format. We study a technique which integrates the knowledge

A new property named RULE has been added in the MIB, which gathers important aspects of the facts and the knowledge base of the embedded expert system. By integrating the knowledge base in resources specifications, system has the power to provide diagnosis of fault network, which can assist engineering trainees, inspectorate staff and professional. Furthermore this paper outlines the development of an expert system prototype based in our propose GDMO+ standard and describes the most important facets, advantages and

This paper is organized as follows. In the section immediately below, we describe the evolution of network management and the role of network management functions. We will examine the management network, including the concepts, major approaches, and management models. It starts with specific applications and work on expert systems in similar fields. We propose a new Intelligent Integrated Management Model and an extension of

base of expert system within the MIB used to manage a network.

drawbacks that were found after prototyping our proposal.

**1. Introduction** 

technique.

OSI model.

**MIB for the Network Management** 

Antonio Martín and Carlos León

*Dpto. Tecnología Electrónica. University of Sevilla* 

