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Proceedings of the ISSEK94 Workshop on Mathematical and Statistical Methods in Artificial Intelligence

  • Conference proceedings
  • © 1995

Overview

Editors:
  1. G. Riccia

    1. University of Udine, Italy

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  2. R. Kruse

    1. University of Braunschweig, Germany

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  3. R. Viertl

    1. Technical University of Wien, Austria

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Part of the book series: CISM International Centre for Mechanical Sciences (CISM, volume 363)

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Table of contents (17 papers)

  1. Front Matter

    Pages ii-viii
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  2. Fuzzy Specification of Uncertain Knowledge and Vague or Imprecise Information

    • H. Bandemer
    Pages 1-21

  3. Some Notes on Possibilistic Learning

    • J. Gebhardt, R. Kruse
    Pages 23-32

  4. Statistics with Fuzzy Data

    • R. Viertl
    Pages 33-49

  5. Operative Controlling Based on Bayesian Networks Using the Kalman Filter Technique

    • H. J. Lenz
    Pages 51-58

  6. Construction, Simulation and Testing of Causal Probabilistic Networks

    • U. G. Oppel, A. Hierle, M. Noormohammadian
    Pages 59-87

  7. Conditional Events and Probability in the Approach to Artificial Intelligence through Coherence

    • R. Scozzafava
    Pages 89-104

  8. Abduction and Induction Based on Non-Monotonic Reasoning

    • F. Bergadano, Ph. Besnard
    Pages 105-118

  9. Independence Relationships in Possibility Theory and Their Application to Learning Belief Networks

    • L. M. de Campos
    Pages 119-130

  10. Using Abduction to Learn Horn Theories

    • D. Gunetti
    Pages 131-146

  11. On Logics of Approximate Reasoning II

    • P. Hájek
    Pages 147-155

  12. Based Revision Operations and Schemes: Semantics, Representation, and Complexity

    • B. Nebel
    Pages 157-170

  13. On the Notion of Similarity in Case-Based Reasoning

    • M. M. Richter
    Pages 171-183

  14. Linear Space Induction in First Order Logic with Relieff

    • U. Pompe, I. Kononenko
    Pages 185-198

  15. Induction of Decision Trees Using Relieff

    • I. Kononenko, E. Simec
    Pages 199-220

  16. Representing Examples by Examples

    • M. Bohanec, I. Bratko
    Pages 221-235

  17. Green Coffee Grading Using Fuzzy Classification

    • F. Suggi Liverani
    Pages 237-245

  18. Learning from Noisy Medical Data: A Comparative Study Based on a Real Diagnostic Problem

    • B. A. Teather, G. Della Riccia, D. Teather
    Pages 247-256
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Keywords

About this book

In recent years it has become apparent that an important part of the theory of Artificial Intelligence is concerned with reasoning on the basis of uncertain, incomplete or inconsistent information. Classical logic and probability theory are only partially adequate for this, and a variety of other formalisms have been developed, some of the most important being fuzzy methods, possibility theory, belief function theory, non monotonic logics and modal logics. The aim of this workshop was to contribute to the elucidation of similarities and differences between the formalisms mentioned above.

Editors and Affiliations

  • University of Udine, Italy

    G. Riccia

  • University of Braunschweig, Germany

    R. Kruse

  • Technical University of Wien, Austria

    R. Viertl

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