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Semantic Selection for Resolution in Clause Graphs

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AI 2002: Advances in Artificial Intelligence (AI 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2557))

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Abstract

In this contribution we present a variant of a resolution theorem prover which selects resolution steps based on the proportion of models a newly generated clause satisfies compared to all models given in a reference class. This reference class is generated from a subset of the initial clause set. Since the empty clause does not satisfy any models, preference is given to such clauses which satisfy few models only. Because computing the number of models is computationally expensive on the one hand, but will remain almost unchanged after the application of one single resolution step on the other hand, we adapt Kowalski’s connection graph method to store the number of models at each link.

The second author likes to thank Norman Foo and the Knowledge Systems Group at the AIDepartmen t of the University of New South Wales for their hospitality.

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Author information

Authors and Affiliations

  1. School of Computer Science, The University of Birmingham, B15 2TT, Birmingham, England

    Seungyeob Choi & Manfred Kerber

Authors
  1. Seungyeob Choi
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  2. Manfred Kerber
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Editor information

Editors and Affiliations

  1. Australian Defence Force Academy, University of New South Wales, ACT 2600, Canberra, Australia

    Bob McKay

  2. Computer Science Laboratory, Australian National University, RSISE Building, ACT 0200, Canberra, Australia

    John Slaney

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© 2002 Springer-Verlag Berlin Heidelberg

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Choi, S., Kerber, M. (2002). Semantic Selection for Resolution in Clause Graphs. In: McKay, B., Slaney, J. (eds) AI 2002: Advances in Artificial Intelligence. AI 2002. Lecture Notes in Computer Science(), vol 2557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36187-1_8

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  • DOI: https://doi.org/10.1007/3-540-36187-1_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00197-3

  • Online ISBN: 978-3-540-36187-9

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