Expert Systems: Principles and ProgrammingIntroduction to Expert Systems 2 The Representation of Knowledge 3 Methods of Inference 4 Reasoning Under Uncertainty 5 Inexact Reasoning 6 Design of Expert Systems 7 Introduction to CLIPS 8 Advanced Pattern Matching 9 Modular Design, Execution Control, and Rule Efficiency 10 Procedural Programming 11 Classes, Instances, and Message-Handlers 12 Expert System Design Examples Appendices A: Some Useful Equivalences B: Some Elementary Quantifiers and The ir Meanings C: Some Set Properties D: CLIPS Support Information E: CLIPS Commands and Functions Summary F: CLIPS BNF G: Software Resources. |
Contents
INTRODUCTION TO EXPERT SYSTEMS | 1 |
THE REPRESENTATION OF KNOWLEDGE | 63 |
METHODS OF INFERENCE | 107 |
Copyright | |
22 other sections not shown
Common terms and phrases
activated agenda algorithm answer antecedent application argument assert attempt-rule attribute axioms backward chaining belief best-color block called certainty factors Chapter CLIPS command crlf cycle decision tree deffacts defined defrule Dempster-Shafer theory determine device E₁ elements emergency fire evidence example execution expert system expression fact fact-list field constraint following rule forward chaining fuzzy logic fuzzy set goal human expert hypothesis inference engine input knowledge engineer knowledge-base language LISP m₁ means membership grade modus ponens multifield value MYCIN numeric-value on-top-of operator OPS5 output partial matches pattern matching phase possible predicate logic premises printout probability problem procedure PROLOG proposition propositional logic PROSPECTOR quantifier raw-value reasoning represent Rete Algorithm retract rule-based rules fired salience semantic semantic net sensor values shown following specific stack statement string subset syllogism symbol syntax Table template theorem uncertainty valid variable