A heuristic search approach to planning with temporally extended preferences
Artificial Intelligence
Solving (Weighted) Partial MaxSAT through Satisfiability Testing
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
The fast downward planning system
Journal of Artificial Intelligence Research
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Soft goals can be compiled away
Journal of Artificial Intelligence Research
MiniMaxSAT: a new weighted Max-SAT solver
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
Partial weighted MaxSAT for optimal planning
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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In this paper, we explore the application of partial weighted MaxSAT techniques for preference-based planning (PBP). To this end, we develop a compact partial weighted MaxSAT encoding for PBP based on the popular SAS+ planning formalism. Our encoding extends a SAS+ based encoding for SAT-based planning, SASE, to allow for the specification of simple preferences. To the best of our knowledge, the SAS+ formalism has never been exploited in the context of PBP. Our MaxSAT-based PBP planner, MSPlan, significantly outperformed the state-of-the-art STRIPS-based MaxSAT approach for PBP with respect to running time, solving more problems in a few cases. Interestingly, when compared to three state-of-the-art heuristic search planners for PBP, MSPlan consistently generated plans with comparable quality, slightly outperforming at least one of these three planners in almost every case. Our results illustrate the effectiveness of our SASE based encoding and suggests that MaxSAT-based PBP is a promising area of research.