Introduction To Expert Systems, 3rd edition

  • Peter Jackson

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Overview

In May 1997, IBM's Deeper Blue defeated the world chess champion Gary Kasparov, showing that an artificial intelligence system can outplay even the most skilled of human experts. Since the first expert systems appeared in the late sixties, we have seen three decades of research and development engineer human knowledge to more practical ends, in a pioneering effort that has integrated diverse areas of cognitive and computer science. Today, expert systems exist in many forms, from medical diagnosis to investment analysis and from counseling to production control.

This third edition of Peter Jackson's best-selling book updates the technological base of expert systems research and embeds those developments in a wide variety of application areas. The earlier chapters have been refocused to take a more practical approach to the basic topics, while the later chapters introduce new topic areas such as case-based reasoning, connectionist systems and hybrid systems. Results in related areas, such as machine learning and reasoning with uncertainty, are also accorded a thorough treatment.

The new edition contains many new examples and exercises, most of which are in CLIPS, a language that combines production rules with object-oriented programming. LISP, PROLOG and C++ are also featured where appropriate. Interesting problems are posed throughout, and are solved in exercises involving the analysis, design and implementation of CLIPS programs.

This book will prove useful to a wide readership including general readers, students and teachers, software engineers and researchers. Its modular structure enables readers to follow a pathway most suited to their needs, providing them with an up-to-date account of expert systems technology.

Peter Jackson is Director of Research at West Group, a division of The Thomson Corporation and the leading provider of information to the US legal market. Peter drives the application of natural language and information retrieval technologies to the information needs of law and business. Previous appointments include Principal Scientist at the McDonnell Douglas Research Laboratories in Saint Louis, Missouri, and Lecturer in the Department of Artificial Intelligence at the University of Edinburgh, Scotland.

Table of contents

1. What Are Expert Systems?
2. An Overview of Artificial Intelligence.
3. Knowledge Representation.
4. Symbolic Computation.
5. Rule-Based Systems.
6. Structured Objects.
7. Object-Oriented Programming.
8. Logic Programming.
9. Representing Uncertainty.
10. Knowledge Acquisition.
11. Heuristic Classification (I).
12. Heuristic Classification (II).
13. Hierarchical Hypothesise and Test.
14. Constructive Problem Solving (I).
15. Constructive Problem Solving (II).
16. Designing for Explanation.
17. Tools for Building Expert Systems.
18. Blackboard Systems.
19. Truth Maintenance Systems.
20. Machine Learning.
21. Belief Networks.
22. Case Based Reasoning.
23. Hybrid Systems.
24. Summary and Conclusion. 
 Appendix:
CLIPS Programming.

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Published by Pearson (February 16th 1999) - Copyright © 1999