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

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

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.

• artificial intelligence
• fuzzy methods
• intelligence
• probability
• probability theory
• statistical methods

• University of Udine, Italy

  G. Riccia

• University of Braunschweig, Germany

  R. Kruse

• Technical University of Wien, Austria

  R. Viertl

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