Cost-Optimal Planning using Weighted MaxSAT
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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.
20th International Conference on Automated Planning and Scheduling: COPLAS'10 Auxiliary Proceedings
© 2010 AAAI Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Use hypertext link for access to conference website.
Knowledge Representation and Machine Learning