This bestselling guide to expert systems has been comprehensively revised to reflect the many recent developments in the field. Written in a clear and entertaining style, it shows how expert systems can be successfully applied to a wide range of applications. Features * coverage of advanced topics including blackboard systems, non-monotonic reasoning and truth-maintenance system * includes programming examples in LISP, OPS5 and other languages, showing how expert systems are developed
Written for the computer science student or more advanced developer interested in expert systems, the new edition of Peter Jackson's
Introduction to Expert Systems provides a truly magisterial tour of several decades of artificial intelligence (AI) and expert system research. This comprehensive book compiles past efforts to get computers to reason like experts as well as explaining how today's expert systems work.
The notable strength of this title is the author's impressive command of the computer science literature in the field of AI and expert systems. In chapter after chapter, the author reviews research-quality (and some production-level) projects that have attempted to reproduce human expertise using computers in such areas as medical diagnosis. Besides explaining essential expert system topics such as knowledge representation and the rules used for arriving at decisions, this book also provides numerous samples using the CLIPS programming language.
This textbook-style treatment of this subject features plenty of mathematical notation (such as the propositional calculus) and exercises at the end to stimulate your own thinking. For the general reader, the author's explanation of the history of AI (which has offered mixed results in the face of high expectations in the 1980s, for instance) is quite accessible and probably worth the price of the book. Rich with detail for the expert, this title shows off the best of expert systems--both in the past and today--as well as the reasonably bright prospects for the future of intelligent machines that think more like human experts. --Richard Dragan
Topics covered: Introduction to expert systems; representing knowledge; history of artificial intelligence (AI) research: Classical, Romantic, and Modern Periods; knowledge representation: STIPS and MYCIN applications; symbolic representation and LISP; rule-based and canonical systems; associative nets and frame systems: graphs, trees, and networks; object-oriented programming and its usefulness in expert systems; programming languages: LOOPS, Flavors, CLOS, CLIPS, C++, and PROLOG; uncertainty and fuzzy logic; knowledge acquisition; heuristic classification; strategies for problem solving; blackboard architectures; machine learning; police networks; case-based reasoning (CBR); hybrid systems; and the future of expert systems.
