Heuristics for Planning with SAT

  • Jussi Rintanen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Planning Graph Horizon Length Current Partial Assignment Clause Learning Variable Selection Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jussi Rintanen
    • 1
  1. 1.NICTA and the Australian National UniversityCanberraAustralia

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