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Succinct Representations of Model Based Belief Revision

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STACS 2000 (STACS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1770))

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Abstract

In this paper, following the approach of Gocic, Kautz, Papadimitriou and Selman (1995), we consider the ability of belief revision operators to succinctly represent a certain set of models. In particular, we show that some of these operators are more efficient than others, even though they have the sane model checking complexity. We show that these operators are partially ordered, i.e. some of them are not comparable. We also strengthen some of the results by Cadoli, Donini, Liberatore and Shaerf (1995) by showing that for some of the so called “model based” operators, a polynomial size representation does not exist even if we allow the new knowledge base to have a non polynomial time model checking (namely, either in NP or in co-NP). Finally, we show that Dalal’s and Weber’s operators can be compiled one into the other via a formalism whose model checking is in NP. All of our results also hold when iterated revision, for one or more of the operators, is considered.

Part of this work has been done while the author was visiting the research center of INRIA Sophia Antipolis (SLOOP project).

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Author information

Authors and Affiliations

  1. Dipartimento di Matematica, Università di Roma “Tor Vergata”, Rome

    Paolo Penna

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  1. Paolo Penna
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Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Dept. of Theoretical Computer Science, Universität Dresden, 01062, Dresden, Germany

    Horst Reichel

  2. Université de Lille, LIFL - UPRESA 8022, CNRS, Bât. M3 - UFR IEEA, 59655, Villeneuve d’ASCQ Cedex, France

    Sophie Tison

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Penna, P. (2000). Succinct Representations of Model Based Belief Revision. In: Reichel, H., Tison, S. (eds) STACS 2000. STACS 2000. Lecture Notes in Computer Science, vol 1770. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46541-3_17

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  • DOI: https://doi.org/10.1007/3-540-46541-3_17

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  • Print ISBN: 978-3-540-67141-1

  • Online ISBN: 978-3-540-46541-6

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