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Learning Default Theories

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Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1638))

Abstract

This paper proposes a method to learn from a set of examples a theory expressed in default logic, more precisely in Lukaszewicz’default logic. The main characteristic of our method is to deal with theories where the definitions of a predicate p and definitions for its negation: ¬p are explicitly and simultaneously learned. This method relies on classical generalization techniques proposed in the field of Inductive Logic Programming and on the notion of credulous/skeptical theorem in Default Logic.

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Author information

Authors and Affiliations

  1. LERIA - University of Angers - France 2, boulevard Lavoisier, F-49045, Angers Cedex 01

    Béatrice Duval & Pascal Nicolas

Authors
  1. Béatrice Duval
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  2. Pascal Nicolas
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University College London, Gower Street, WC1E 6BT, London, UK

    Anthony Hunter

  2. Department of Electronic Engineering, University of London, Queen Mary and Westfield College, E1 4NS, London, UK

    Simon Parsons

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© 1999 Springer-Verlag Berlin Heidelberg

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Duval, B., Nicolas, P. (1999). Learning Default Theories. In: Hunter, A., Parsons, S. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1999. Lecture Notes in Computer Science(), vol 1638. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48747-6_14

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  • DOI: https://doi.org/10.1007/3-540-48747-6_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66131-3

  • Online ISBN: 978-3-540-48747-0

  • eBook Packages: Springer Book Archive

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