Partial Weighted MaxSAT for Optimal Planning

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.

• Type: Journal
• Year: 2010
• Where: PRICAI
• Authors: Nathan Robinson, Charles Gretton, Duc Nghia Pham, Abdul Sattar
• Keywords: Artificial Intelligence, General-purpose Automated Reasoning, Partial Weighted Maxsat, PRICAI 2010, Propositional Planning Problems