Fuzzy sets were for a long time not accepted by the AI community. Now they have become highly evolved and their techniques are well established. This book will teach the reader how to construct a fuzzy expert system to solve real-world problems. After a general discussion of expert systems, the basic fuzzy math required is presented first, requiring little more math background than high-school algebra. This book will fill a void in the market because although there are many books on expert systems, none devote more than a few pages to the notion of fuzzy sets and their applications in this domain. Therefore their use in this book is timely and should be well received.pbrThe book is designed as a text and has ample problems with solutions, a solutions manual and an accompanying program on our ftp site. Coverage is accessible to practitioners and academic readers alike.brpPreface.pb1 Introduction./bp1.1 Characteristics of Expert Systems.p1.2 Neural Nets.p1.3 Symbolic Reasoning.p1.4 Developing a Rule-Based Expert System.p1.5 Fuzzy Rule-Based Systems.p1.6 Problems in Learning How to Construct Fuzzy Expert Systems.p1.7 Tools for Learning How to Construct Fuzzy Expert Systems.p1.8 Auxiliary Reading.p1.9 Summary.p1.10 Questions.pb2 Rule-Based Systems: Overview./bp2.1 Expert Knowledge: Rules and Data.p2.2 Rule Antecedent and Consequent.p2.3 Data-Driven Systems.p2.4 Run and Command Modes.p2.5 Forward and Backward Chaining.p2.6 Program Modularization and Blackboard Systems.p2.7 Handling Uncertainties in an Expert System.p2.8 Summary.p2.9 Questions.pb3 Fuzzy Logic, Fuzzy Sets, and Fuzzy Numbers: I./bp3.1 Classical Logic.p3.2 Elementary Fuzzy Logic and Fuzzy Propositions.p3.3 Fuzzy Sets.p3.4 Fuzzy Relations.p3.5 Truth Value of Fuzzy Propositions.p3.6 Fuzzification and Defuzzification.p3.7 Questions.pb4 Fuzzy Logic, Fuzzy Sets, and Fuzzy Numbers: II./bp4.1 Introduction.p4.2 Algebra of F@a?®záH ¾Û€