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Branching Rules for Satisfiability Analysed with Factor Analysis

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AI 2007: Advances in Artificial Intelligence (AI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4830))

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Abstract

Factor analysis is a statistical technique for reducing the number of factors responsible for a matrix of correlations to a smaller number of factors that may reflect underlying variables. Earlier experiments with constraint satisfaction problems (CSPs) using factor analysis suggested that there are only a few distinct principles of heuristic action. Here, this work is extended to the analysis of branching rules for SAT problems using the Davis-Putnam algorithm. These experiments show that just as with CSPs, there seem to be two basic actions that distinguish heuristics, characterised as building up of contention and propagation of effects to the uninstantiated portion of the problem.

This was supported by Science Foundation Ireland under Grant 00/PI.1/C075.

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Authors and Affiliations

  1. Cork Constraint Computation Centre and Department of Computer Science, University College Cork, Cork, Ireland

    Richard J. Wallace

  2. Institute of Transport and Logistics Studies, Faculty of Economics and Business, University of Sydney, Sydney, NSW 2006, Australia

    Stuart Bain

Authors
  1. Richard J. Wallace
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  2. Stuart Bain
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Mehmet A. Orgun John Thornton

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© 2007 Springer-Verlag Berlin Heidelberg

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Wallace, R.J., Bain, S. (2007). Branching Rules for Satisfiability Analysed with Factor Analysis. In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_96

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  • DOI: https://doi.org/10.1007/978-3-540-76928-6_96

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76926-2

  • Online ISBN: 978-3-540-76928-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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