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International Conference on Principles and Practice of Constraint Programming

CP 2010: Principles and Practice of Constraint Programming – CP 2010 pp 414–428Cite as

Heuristics for Planning with SAT

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6308))

Abstract

Generic SAT solvers have been very successful in solving hard combinatorial problems in various application areas, including AI planning. There is potential for improved performance by making the SAT solving process more application-specific. In this paper we propose a variable selection strategy for AI planning. The strategy is based on generic principles about properties of plans, and its performance with standard planning benchmarks often substantially improves on generic variable selection heuristics used in SAT solving, such as the VSIDS strategy. These improvements lift the efficiency of SAT based planning to the same level as best planners that use other search methods.

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Author information

Authors and Affiliations

  1. NICTA and the Australian National University, Canberra, Australia

    Jussi Rintanen

Authors
  1. Jussi Rintanen
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Royal Holloway, University of London, Egham, TW20 0EX, Surrey, United Kingdom

    David Cohen

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Rintanen, J. (2010). Heuristics for Planning with SAT. In: Cohen, D. (eds) Principles and Practice of Constraint Programming – CP 2010. CP 2010. Lecture Notes in Computer Science, vol 6308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15396-9_34

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  • DOI: https://doi.org/10.1007/978-3-642-15396-9_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15395-2

  • Online ISBN: 978-3-642-15396-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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