Until recently, fuzzy logic was the intellectual plaything of a handful of researchers. Now it is being used to enhance the power of intelligent systems, as well as improve the performance and reduce the cost of intelligent and "smart" products appearing in the commercial market. Fuzzy Expert Systems focuses primarily on the theory of fuzzy expert systems and their applications in science and engineering. In doing so, it provides the first comprehensive study of "soft" expert systems and applications for those systems. Topics covered include general purpose fuzzy expert systems, processing imperfect information using structured frameworks, the fuzzy linguistic inference network generator, fuzzy associative memories, the role of approximate reasoning in medical expert systems, MILORD (a fuzzy expert systems shell), and COMAX (an autonomous fuzzy expert system for tactical communications networks. Fuzzy Expert Systems provides an invaluable reference resource for researchers and students in artificial intelligence (AI) and approximate reasoning (AR), as well as for other researchers looking for methods to apply similar tools in their own designs of intelligent systems.

The monograph offers a view on Rough Mereology, a tool for reasoning under uncertainty, which goes back to Mereology, formulated in terms of parts by Lesniewski, and borrows from Fuzzy Set Theory and Rough Set Theory ideas of the containment to a degree. The result is a theory based on the notion of a part to a degree. One can invoke here a formula Rough: Rough Mereology : Mereology = Fuzzy Set Theory : Set Theory. As with Mereology, Rough Mereology finds important applications in problems of Spatial Reasoning, illustrated in this monograph with examples from Behavioral Robotics. Due to its involvement with concepts, Rough Mereology offers new approaches to Granular Computing, Classifier and Decision Synthesis, Logics for Information Systems, and are--formulation of well--known ideas of Neural Networks and Many Agent Systems. All these approaches are discussed in this monograph. To make the exposition self--contained, underlying notions of Set Theory, Topology, and Deductive and Reductive Reasoning with emphasis on Rough and Fuzzy Set Theories along with a thorough exposition of Mereology both in Lesniewski and Whitehead--Leonard--Goodman--Clarke versions are discussed at length. It is hoped that the monograph offers researchers in various areas of Artificial Intelligence a new tool to deal with analysis of relations among concepts.

Hier lernen Sie, Expertensysteme auf der Basis von Fuzzy Logic zu konstruieren, die sich für den praktischen Einsatz eignen. Expertensysteme werden zunächst allgemein definiert, und die zugrundeliegende Mathematik wird eingeführt. Regelbasierte Systeme werden gründlicher besprochen als in jedem anderen Buch mit ähnlichem Thema. Am Ende jedes Kapitels können Sie Ihren Wissensstand anhand von Übungsaufgaben überprüfen. Von einem zugehörigen ftp-Server können Sie Ergänzungsmaterial abrufen. Für Praktiker und Forscher aus dem akademischen Umfeld gleichermaßen geeignet!

Assessing the degree to which two objects, an object and a query, or two concepts are similar or compatible is a fundamental component of human reasoning and consequently is critical in the development of automated diagnosis, classification, information retrieval and decision systems. The assessment of similarity has played an important role in such diverse disciplines such as taxonomy, psychology, and the social sciences. Each discipline has proposed methods for quantifying similarity judgments suitable for its particular applications. This book presents a unified approach to quantifying similarity and compatibility within the framework of fuzzy set theory and examines the primary importance of these concepts in approximate reasoning. Examples of the application of similarity measures in various areas including expert systems, information retrieval, and intelligent database systems are provided.

This volume gives readers a complete picture of fuzzy approximate reasoning and uncertainty reasoning based on evidence theory. The topics are systematically treated from fundamentals to advanced issues. Numerous examples illustrate the concepts. The work decribes and analyzes various approaches regarding fuzzy approximate reasoning including fuzzy logics, uncertainty representation, and fuzzy inference patterns. Approximate reasoning based on evidence theory is mainly resulted from the authors' own research work. The book can be used as a textbook for courses in uncertainty reasoning as well as a quick reference on techniques for managing uncertainty for any one working in the areas of expert systems.

Fuzzy set theory is a mathematical structure for representing uncertainty. Modern intelligent systems must combine knowledge based on techniques for gathering and processing information with methods of approximate reasoning. This enables an intelligent system to better emulate human decision-making in uncertain environments. Complimentary to the text is a software package containing executable FEST code. The text contains a detailed description of FEST and its coherent and systematic exploitation.

This book contains the refereed proceedings of the 8th International Conference on Database and Expert Systems Applications, DEXA '97, held in Toulouse, France, September 1997. The 62 revised full papers presented in the book, together with three invited contributions, were selected from a total of 159 submissions. The papers are organized in sections on modeling, object-oriented databases, active and temporal aspects, images, integrity constraints, multimedia databases, deductive databases and knowledge-based systems, allocation concepts, data interchange, digital libraries, transaction concepts, learning issues, optimization and performance, query languages, maintenance, federated databases, uncertainty handling and qualitative reasoning, and software engineering and reusable software.

The present volume contains edited versions of the communications presented at an International Workshop on "Expert Systems in Production Engineering", held in Spa, Belgium, in 1986. Introductory papers on Artificial Intelligence and Expert Systems are complemented by case studies of Expert Systems in practice, primarily, in the area of Mechanical Engineering and discussions of the possibilities and the limitations of Expert Systems.

3. Textbook for a course in expert systems,if an emphasis is placed on Chapters 1 to 3 and on a selection of material from Chapters 4 to 7. There is also the option of using an additional commercially available sheU for a programming project. In assigning a programming project, the instructor may use any part of a great variety of books covering many subjects, such as car repair. Instructions for mostofthe "weekend mechanic" books are close stylisticaUy to expert system rules. Contents Chapter 1 gives an introduction to the subject matter; it briefly presents basic concepts, history, and some perspectives ofexpert systems. Then itpresents the architecture of an expert system and explains the stages of building an expert system. The concept of uncertainty in expert systems and the necessity of deal ing with the phenomenon are then presented. The chapter ends with the descrip tion of taxonomy ofexpert systems. Chapter 2 focuses on knowledge representation. Four basic ways to repre sent knowledge in expert systems are presented: first-order logic, production sys tems, semantic nets, and frames. Chapter 3 contains material about knowledge acquisition. Among machine learning techniques, a methodofrule learning from examples is explained in de tail. Then problems ofrule-base verification are discussed. In particular, both consistency and completeness oftherule base are presented.