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Conditionals, Information, and Inference

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

  • Conference proceedings
  • © 2005

Overview

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

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: WCII 2002.

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Table of contents (12 papers)

  1. Invited Papers

  2. Regular Papers

Other volumes

  1. Conditionals, Information, and Inference

Keywords

About this book

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.

Editors and Affiliations

  • Dept. of Computer Science, TU Dortmund, Dortmund, Germany

    Gabriele Kern-Isberner

  • FernUniversität in Hagen, Fachbereich Wirtschaftswissenschaft, Lehrstuhl für BWL, insb. Operations Research, Hagen, Germany

    Wilhelm Rödder

  • FernUniversität in Hagen, Fachbereich Wirtschaftswissenschaft, Lehrstuhl BWL, insb. Operations Research, Hagen, Germany

    Friedhelm Kulmann

Bibliographic Information

  • Book Title: Conditionals, Information, and Inference

  • Book Subtitle: International Workshop, WCII 2002, Hagen, Germany, May 13-15, 2002, Revised Selected Papers

  • Editors: Gabriele Kern-Isberner, Wilhelm Rödder, Friedhelm Kulmann

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/b107184

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2005

  • Softcover ISBN: 978-3-540-25332-7Published: 18 May 2005

  • eBook ISBN: 978-3-540-32235-1Published: 13 May 2005

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XII, 219

  • Topics: Artificial Intelligence, Mathematical Logic and Formal Languages

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