Advertisement

Managing Uncertainty in Expert Systems

  • Jerzy W. Grzymala-Busse

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Jerzy W. Grzymala-Busse
    Pages 1-12
  3. Jerzy W. Grzymala-Busse
    Pages 13-42
  4. Jerzy W. Grzymala-Busse
    Pages 43-76
  5. Jerzy W. Grzymala-Busse
    Pages 77-102
  6. Jerzy W. Grzymala-Busse
    Pages 103-126
  7. Jerzy W. Grzymala-Busse
    Pages 127-180
  8. Jerzy W. Grzymala-Busse
    Pages 181-207
  9. Back Matter
    Pages 209-224

About this book

Introduction

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.

Keywords

Frames addition algorithms expert system fuzzy fuzzy logic fuzzy sets knowledge knowledge representation learning machine learning programming set theory uncertainty verification

Authors and affiliations

  • Jerzy W. Grzymala-Busse
    • 1
  1. 1.University of KansasUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-3982-7
  • Copyright Information Kluwer Academic Publishers 1991
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-6779-6
  • Online ISBN 978-1-4615-3982-7
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site
Industry Sectors
Pharma
Materials & Steel
Automotive
Chemical Manufacturing
Health & Hospitals
Biotechnology
Finance, Business & Banking
Electronics
IT & Software
Telecommunications
Consumer Packaged Goods
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences
Engineering