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Managing Uncertainty in Expert Systems

  • Book
  • © 1991

Overview

Authors:
  1. Jerzy W. Grzymala-Busse

    1. University of Kansas, USA

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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 143)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xxi
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  2. Introduction

    • Jerzy W. Grzymala-Busse
    Pages 1-12

  3. Knowledge Representation

    • Jerzy W. Grzymala-Busse
    Pages 13-42

  4. Knowledge Acquisition

    • Jerzy W. Grzymala-Busse
    Pages 43-76

  5. One-Valued Quantitative Approaches

    • Jerzy W. Grzymala-Busse
    Pages 77-102

  6. Two-Valued Quantitative Approaches

    • Jerzy W. Grzymala-Busse
    Pages 103-126

  7. Set-Valued Quantitative Approaches

    • Jerzy W. Grzymala-Busse
    Pages 127-180

  8. Qualitative Approaches

    • Jerzy W. Grzymala-Busse
    Pages 181-207

  9. Back Matter

    Pages 209-224
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Keywords

About this book

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.

Authors and Affiliations

  • University of Kansas, USA

    Jerzy W. Grzymala-Busse

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