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Mathematical Models for Handling Partial Knowledge in Artificial Intelligence

  • Book
  • © 1995

Overview

Editors:
  1. Giulianella Coletti

    1. University of Perugia, Perugia, Italy

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  2. Didier Dubois

    1. University of Toulouse, Toulouse, France

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  3. Romano Scozzafava

    1. University “La Sapienza”, Rome, Italy

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Table of contents (20 chapters)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. Invited Papers

    1. Ellsberg Paradox Intuition and Choquet Expected Utility

      • Alain Chateauneuf
      Pages 1-20

    2. Fuzzy Logic as Logic

      • Petr Hájek
      Pages 21-30

    3. Mathematical Foundations of Evidence Theory

      • Jürg Kohlas
      Pages 31-64

    4. Semantics for Uncertain Inference Based on Statistical Knowledge

      • Henry E. Kyburg Jr.
      Pages 65-81

    5. Prospects and Problems in Applying the Fundamental Theorem of Prevision as an Expert System: An Example of Learning About Parole Decisions

      • Frank Lad, Ian Coope
      Pages 83-100

    6. Coherent Prevision as a Linear Functional without an Underlying Measure Space: The Purely Arithmetic Structure of Logical Relations Among Conditional Quantities

      • Frank Lad
      Pages 101-111

    7. Revision Rules for Convex Sets of Probabilities

      • Serafín Moral, Nic Wilson
      Pages 113-127

    8. Some Mathematical Tools for Decision Making under Partial Knowledge

      • Hung T. Nguyen
      Pages 129-156

    9. From Bayesian Networks to Causal Networks

      • Judea Pearl
      Pages 157-182

  3. Contributed Papers

    1. Generalized Concept of Atoms for Conditional Events

      • Andrea Capotorti
      Pages 183-189

    2. Checking the Coherence of Conditional Probabilities in Expert Systems: Remarks and Algorithms

      • Giuseppe Di Biase, Antonio Maturo
      Pages 191-200

    3. A Hyperstructure of Conditional Events for Artificial Intelligence

      • Serena Doria, Antonio Maturo
      Pages 201-208

    4. Possibilistic Logic and Plausible Inference

      • Didier Dubois, Henri Prade
      Pages 209-228

    5. Probability Logic as a Fuzzy Logic

      • Giangiacomo Gerla
      Pages 229-230

    6. Algorithms for Precise and Imprecise Conditional Probability Assessments

      • Angelo Gilio
      Pages 231-254

    7. A Valuation-Based Architecture for Assumption-Based Reasoning

      • Rolf Haenni
      Pages 255-257

    8. Computing Symbolic Support Functions by Classical Theorem-Proving Techniques

      • Urs Hänni
      Pages 259-261

    9. Inconsistent Knowledge Integration in a Probabilistic Model

      • Radim Jiroušek, Jiří Vomlel
      Pages 263-270

    10. Conditional and Comparative Probabilities in Artificial Intelligence

      • Paolo Vicig
      Pages 271-280
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Keywords

About this book

Knowledge acquisition is one of the most important aspects influencing the quality of methods used in artificial intelligence and the reliability of expert systems. The various issues dealt with in this volume concern many different approaches to the handling of partial knowledge and to the ensuing methods for reasoning and decision making under uncertainty, as applied to problems in artificial intelligence. The volume is composed of the invited and contributed papers presented at the Workshop on Mathematical Models for Handling Partial Knowledge in Artificial Intelligence, held at the Ettore Majorana Center for Scientific Culture of Erice (Sicily, Italy) on June 19-25, 1994, in the framework of the International School of Mathematics "G.Stampacchia". It includes also a transcription of the roundtable held during the workshop to promote discussions on fundamental issues, since in the choice of invited speakers we have tried to maintain a balance between the various schools of knowl­ edge and uncertainty modeling. Choquet expected utility models are discussed in the paper by Alain Chateauneuf: they allow the separation of perception of uncertainty or risk from the valuation of outcomes, and can be of help in decision mak­ ing. Petr Hajek shows that reasoning in fuzzy logic may be put on a strict logical (formal) basis, so contributing to our understanding of what fuzzy logic is and what one is doing when applying fuzzy reasoning.

Editors and Affiliations

  • University of Perugia, Perugia, Italy

    Giulianella Coletti

  • University of Toulouse, Toulouse, France

    Didier Dubois

  • University “La Sapienza”, Rome, Italy

    Romano Scozzafava

Bibliographic Information

  • Book Title: Mathematical Models for Handling Partial Knowledge in Artificial Intelligence

  • Editors: Giulianella Coletti, Didier Dubois, Romano Scozzafava

  • DOI: https://doi.org/10.1007/978-1-4899-1424-8

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 1995

  • Hardcover ISBN: 978-0-306-45076-1Published: 31 August 1995

  • Softcover ISBN: 978-1-4899-1426-2Published: 30 May 2013

  • eBook ISBN: 978-1-4899-1424-8Published: 29 June 2013

  • Edition Number: 1

  • Number of Pages: IX, 308

  • Number of Illustrations: 3 b/w illustrations

  • Topics: Statistics, general, Mathematics, general, Management of Computing and Information Systems

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