Partial weighted MaxSAT for optimal planning

Authors:
Nathan Robinson;Charles Gretton;Duc Nghia Pham;Abdul Sattar
Affiliations:
Queensland Research Lab, NICTA and Institute for Integrated and Intelligent Systems, Griffith University, QLD, Australia;School of Computer Science, University of Birmingham;Queensland Research Lab, NICTA and Institute for Integrated and Intelligent Systems, Griffith University, QLD, Australia;Queensland Research Lab, NICTA and Institute for Integrated and Intelligent Systems, Griffith University, QLD, Australia
Venue:
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Year:
2010

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Abstract

We consider the problem of computing optimal plans for propositional planning problems with action costs. In the spirit of leveraging advances in general-purpose automated reasoning for that setting, we develop an approach that operates by solving a sequence of partial weighted MaxSAT problems, each of which corresponds to a step-bounded variant of the problem at hand. Our approach is the first SAT-based system in which a proof of cost-optimality is obtained using a MaxSAT procedure. It is also the first system of this kind to incorporate an admissible planning heuristic. We perform a detailed empirical evaluation of our work using benchmarks from a number of International Planning Competitions.