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Preference-Based Planning via MaxSAT

  • Farah Juma
  • Eric I. Hsu
  • Sheila A. McIlraith
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7310)

Abstract

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.

Keywords

Problem Instance Classical Planning Soft Goal Simple Preference Soft Clause 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Farah Juma
    • 1
  • Eric I. Hsu
    • 1
  • Sheila A. McIlraith
    • 1
  1. 1.Department of Computer ScienceUniversity of TorontoTorontoCanada

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