Conditionals, Information, and Inference

International Workshop, WCII 2002, Hagen, Germany, May 13-15, 2002, Revised Selected Papers

  • Gabriele Kern-Isberner
  • Wilhelm Rödder
  • Friedhelm Kulmann

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3301)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 3301)

Table of contents

  1. Front Matter
  2. Invited Papers

    1. Didier Dubois, Hélène Fargier, Henri Prade
      Pages 38-58
  3. Regular Papers

    1. Emil Weydert
      Pages 65-85
    2. Rainer Osswald
      Pages 108-130
    3. Piotr Chrzastowski-Wachtel, Jerzy Tyszkiewicz
      Pages 131-151
    4. Christoph Beierle, Gabriele Kern-Isberner
      Pages 162-179
    5. Manfred Schramm, Bertram Fronhöfer
      Pages 200-218
  4. Back Matter

About these proceedings

Introduction

Conditionals are fascinating and versatile objects of knowledge representation. On the one hand, they may express rules in a very general sense, representing, for example, plausible relationships, physical laws, and social norms. On the other hand, as default rules or general implications, they constitute a basic tool for reasoning, even in the presence of uncertainty. In this sense, conditionals are intimately connected both to information and inference. Due to their non-Boolean nature, however, conditionals are not easily dealt with. They are not simply true or false — rather, a conditional “if A then B” provides a context, A, for B to be plausible (or true) and must not be confused with “A entails B” or with the material implication “not A or B.” This ill- trates how conditionals represent information, understood in its strict sense as reduction of uncertainty. To learn that, in the context A, the proposition B is plausible, may reduce uncertainty about B and hence is information. The ab- ity to predict such conditioned propositions is knowledge and as such (earlier) acquired information. The ?rst work on conditional objects dates back to Boole in the 19th c- tury, and the interest in conditionals was revived in the second half of the 20th century, when the emerging Arti?cial Intelligence made claims for appropriate formaltoolstohandle“generalizedrules.”Sincethen,conditionalshavebeenthe topic of countless publications, each emphasizing their relevance for knowledge representation, plausible reasoning, nonmonotonic inference, and belief revision.

Keywords

Bayesian network bayesian networks belief revision cognition conditional objects conditionals conditioned propositions inference knowledge representation logic non monotonic logic plausible reasoning practical reasoning probabilistic reasoning

Editors and affiliations

  • Gabriele Kern-Isberner
    • 1
  • Wilhelm Rödder
    • 2
  • Friedhelm Kulmann
    • 3
  1. 1.Dept. of Computer ScienceTU DortmundDortmundGermany
  2. 2.FernUniversität in Hagen, Fachbereich Wirtschaftswissenschaft, Lehrstuhl für BWL, insb. Operations ResearchHagenGermany
  3. 3.FernUniversität in Hagen, Fachbereich Wirtschaftswissenschaft, Lehrstuhl BWL, insb. Operations ResearchHagenGermany

Bibliographic information

  • DOI https://doi.org/10.1007/b107184
  • Copyright Information Springer-Verlag Berlin Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-25332-7
  • Online ISBN 978-3-540-32235-1
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book
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