Introduction to Expert SystemsThis 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 |
Contents
Background | 3 |
Overview of Artificial Intelligence | 15 |
DENDRAL and MYCIN | 35 |
Copyright | |
22 other sections not shown
Common terms and phrases
abstraction actions algorithm applications approach architecture artificial intelligence associated atoms behaviour belief blackboard blackboard systems breadth-first search called certainty factors Chapter Clancey clause COMMON LISP components concept constraints construction context data structures database decision Dempster-Shafer theory DENDRAL depth-first search derived diagnosis disease encoding environment evaluation example expert systems Figure formal formulas frame function fuzzy given goal heuristic heuristic classification hierarchy hypotheses implementation inference interpretation kind knowledge acquisition knowledge base knowledge elicitation knowledge engineer knowledge representation lambda LISP logic match method modules MYCIN nodes object-oriented programming objects operators OPS5 organism parameters particular perform predicate probability probability theory problem solving procedures production rules programming language PROLOG properties proposition prototype reasoning represent representation language rule set S-expression search space slot solution strategy subgoals symbol symptoms task theorem theory tree typically variables version space