Inference methods for a pseudo-boolean satisfiability solver

  • Authors:

  • Heidi E. Dixon;Matthew L. Ginsberg

  • Affiliations:

  • CIRL, 1269 University of Oregon, Eugene, OR;CIRL, 1269 University of Oregon, Eugene, OR

  • Venue:
  • Eighteenth national conference on Artificial intelligence
  • Year:
  • 2002

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Abstract

We describe two methods of doing inference during search for a pseudo-Boolean version of the RELSAT method. One inference method is the pseudo-Boolean equivalent of learning. A new constraint is learned in response to a contradiction with the purpose of eliminating the set of assignments that caused the contradiction. We show that the obvious way of extending learning to pseudo-Boolean is inadequate and describe a better solution. We also describe a second inference method used by the Operations Research community. The method cannot be applied to the standard resolution-based AI algorithms, but is useful for pseudo-Boolean versions of the same AI algorithms. We give experimental results showing that the pseudo-Boolean version of RELSAT outperforms its clausal counterpart on problems from the planning domain.