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Footprints of Conditionals

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Mechanizing Mathematical Reasoning

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2605))

Abstract

Probabilistic conditionals are a powerful means of representing commonsense and expert knowledge. By viewing probabilistic conditionals as an institution, we obtain a formalization of probabilistic conditionals as a logical system. Using the framework of institutions, we phrase a general representation problem that is closely related to the selection of preferred models. The problem of discovering probabilistic conditionals from data can be seen as an instance of the inverse representation problem, thereby considering knowledge discovery as an operation inverse to inductive knowledge representation. These concepts are illustrated using the well-known probabilistic principle of maximum entropy for which we sketch an approach to solve the inverse representation problem.

The research reported here was partially supported by the DFG – Deutsche Forschungsgemeinschaft within the Condor-project under grant BE 1700/5-1.

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Authors and Affiliations

  1. Praktische Informatik VIII – Wissensbasierte Systeme, Fachbereich Informatik, FernUniversität Hagen, D-58084, Hagen, Germany

    Christoph Beierle & Gabriele Kern-Isberner

Authors
  1. Christoph Beierle
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  2. Gabriele Kern-Isberner
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Editor information

Editors and Affiliations

  1. DFKI, Stuhlsatzenhausweg 3, D-66123, Saarbrücken, Germany

    Dieter Hutter

  2. German Research Center for Artificial Intelligence, DFKI GmbH,  

    Werner Stephan

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Beierle, C., Kern-Isberner, G. (2005). Footprints of Conditionals. In: Hutter, D., Stephan, W. (eds) Mechanizing Mathematical Reasoning. Lecture Notes in Computer Science(), vol 2605. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32254-2_6

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  • DOI: https://doi.org/10.1007/978-3-540-32254-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25051-7

  • Online ISBN: 978-3-540-32254-2

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