Engineering Intelligent Hybrid Multi-Agent Systems is about building intelligent hybrid systems. Included is coverage of applications and design concepts related to fusion systems, transformation systems and combination systems. These applications are in areas involving hybrid configurations of knowledge-based systems, case-based reasoning, fuzzy systems, artificial neural networks, genetic algorithms, and in knowledge discovery and data mining. Through examples and applications a synergy of these subjects is demonstrated.
The authors introduce a multi-agent architectural theory for engineering intelligent associative hybrid systems. The architectural theory is described at both the task structure level and the computational level. This problem-solving architecture is relevant for developing knowledge agents and information agents.
An enterprise-wide system modeling framework is outlined to facilitate forward and backward integration of systems developed in the knowledge, information, and data engineering layers of an organization. In the modeling process, software engineering aspects like agent oriented analysis, design and reuse are developed and described.
Engineering Intelligent Hybrid Multi-Agent Systems is the first book in the field to provide details of a multi-agent architecture for building intelligent hybrid systems.
|Product dimensions:||6.10(w) x 9.25(h) x 0.04(d)|
Table of Contents
Preface. 1. Why Intelligent Hybrid Systems. 2. Methodologies. 3. Intelligent Fusion and Transformation Systems. 4. Intelligent Combination Systems. 5. Knowledge Discovery, Data Mining and Hybrid Systems. 6. Association Systems - Task Structure Level Associative Hybrid Architecture. 7. Intelligent Multi-Agent Hybrid Computational Architecture - Part I. 8. Intelligent Multi-Agent Hybrid Computational Architecture - Part II. 9. Alarm Processing - An Application of IMAHDA. 10. Agent Oriented Analysis and Design of the RTAPS - Part I. 11. Agent Oriented Analysis and Design of the RTAPS - Part II. 12. RTAPS Implementation. 13. From Data Repositories to Knowledge Repositories. 14. IMAHDA Revisited. Appendices: A. Input Features of the Animal Domain. B. Classes in the Animal Domain. C. TTS Power Network. D. TTS Substation Power Network. E. Real Time Alarm Data. Index.