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Uncertainty and Vagueness in Knowledge Based Systems

Numerical Methods

  • Rudolf Kruse
  • Erhard Schwecke
  • Jochen Heinsohn

Part of the Artificial Intelligence book series (AI)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 1-8
  3. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 9-27
  4. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 29-44
  5. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 45-83
  6. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 85-117
  7. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 119-178
  8. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 179-209
  9. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 211-223
  10. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 225-259
  11. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 261-277
  12. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 279-298
  13. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 299-370
  14. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 371-414
  15. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 415-445
  16. Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn
    Pages 447-453
  17. Back Matter
    Pages 455-494

About this book

Introduction

The primary aim of this monograph is to provide a formal framework for the representation and management of uncertainty and vagueness in the field of artificial intelligence. It puts particular emphasis on a thorough analysis of these phenomena and on the development of sound mathematical modeling approaches. Beyond this theoretical basis the scope of the book includes also implementational aspects and a valuation of existing models and systems. The fundamental ambition of this book is to show that vagueness and un­ certainty can be handled adequately by using measure-theoretic methods. The presentation of applicable knowledge representation formalisms and reasoning algorithms substantiates the claim that efficiency requirements do not necessar­ ily require renunciation of an uncompromising mathematical modeling. These results are used to evaluate systems based on probabilistic methods as well as on non-standard concepts such as certainty factors, fuzzy sets or belief functions. The book is intended to be self-contained and addresses researchers and practioneers in the field of knowledge based systems. It is in particular suit­ able as a textbook for graduate-level students in AI, operations research and applied probability. A solid mathematical background is necessary for reading this book. Essential parts of the material have been the subject of courses given by the first author for students of computer science and mathematics held since 1984 at the University in Braunschweig.

Keywords

Applied probability Fuzzy Logik Ungenaues Schließen Wahrscheinlichkeitstheorie Wissensaufbe algorithms artificial intelligence fuzzy sets intelligence knowledge knowledge base knowledge representation modeling operations research uncertainty

Authors and affiliations

  • Rudolf Kruse
    • 1
  • Erhard Schwecke
    • 1
  • Jochen Heinsohn
    • 2
  1. 1.Department of Computer ScienceTechnical University of BraunschweigBraunschweigGermany
  2. 2.German Research Center for Artificial Intelligence (DFKI)Saarbrücken 11Germany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-76702-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 1991
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-76704-3
  • Online ISBN 978-3-642-76702-9
  • Series Print ISSN 1431-0066
  • Buy this book on publisher's site