Uncertainty and Vagueness in Knowledge Based Systems

1. Front Matter

   Pages i-xi

   PDF

2. General Considerations of Uncertainty and Vagueness

   • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

   Pages 1-8

3. Introduction

   • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

   Pages 9-27

4. Vague Data

   • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

   Pages 29-44

5. Probability Theory

   • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

   Pages 45-83

6. Random Sets

   • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

   Pages 85-117

7. Mass Distributions

   • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

   Pages 119-178

8. On Graphical Representations

   • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

   Pages 179-209

9. Modeling Aspects

   • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

   Pages 211-223

10. Heuristic Models

    • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

    Pages 225-259

11. Fuzzy Set Based Models

    • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

    Pages 261-277

12. Reasoning with L-Sets

    • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

    Pages 279-298

13. Probability Based Models

    • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

    Pages 299-370

14. Models Based on the Dempster-Shafer Theory of Evidence

    • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

    Pages 371-414

15. Reasoning with Mass Distributions

    • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

    Pages 415-445

16. Related Research

    • Rudolf Kruse, Erhard Schwecke, Jochen Heinsohn

    Pages 447-453

17. Back Matter

    Pages 455-494

    PDF

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.

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

• Department of Computer Science, Technical University of Braunschweig, Braunschweig, Germany

  Rudolf Kruse, Erhard Schwecke

• German Research Center for Artificial Intelligence (DFKI), Saarbrücken 11, Germany

  Jochen Heinsohn

Policies and ethics