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.
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References
Ansótegui, C., Bonet, M.L., Levy, J.: Solving (Weighted) Partial MaxSAT through Satisfiability Testing. In: Kullmann, O. (ed.) SAT 2009. LNCS, vol. 5584, pp. 427–440. Springer, Heidelberg (2009)
Baier, J.A., Bacchus, F., McIlraith, S.A.: A heuristic search approach to planning with temporally extended preferences. AIJ 173(5-6), 593–618 (2009)
Baier, J.A., McIlraith, S.A.: Planning with preferences. AI Magazine 29(4), 25–36 (2008)
Berre, D.L., Parrain, A.: The SAT4J library, release 2.2. Journal on Satisfiability, Boolean Modeling and Computation 7, 59–64 (2010)
Coles, A.J., Coles, A.: LPRPG-P: Relaxed plan heuristics for planning with preferences. In: ICAPS (2011)
Gerevini, A., Haslum, P., Long, D., Saetti, A., Dimopoulos, Y.: Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners. Artificial Intelligence 173(5-6), 619–668 (2009)
Giunchiglia, E., Maratea, M.: A pseudo-boolean approach for solving planning problems with IPC simple preferences. In: COPLAS, pp. 23–31 (2010)
Helmert, M.: The Fast Downward planning system. JAIR 26, 191–246 (2006)
Heras, F., Larrosa, J., Oliveras, A.: MiniMaxSat: A New Weighted Max-SAT Solver. In: Marques-Silva, J., Sakallah, K.A. (eds.) SAT 2007. LNCS, vol. 4501, pp. 41–55. Springer, Heidelberg (2007)
Hsu, C.-W., Wah, B., Huang, R., Chen, Y.: Constraint partitioning for solving planning problems with trajectory constraints and goal preferences. In: IJCAI, pp. 1924–1929 (2007)
Huang, R., Chen, Y., Zhang, W.: A novel transition based encoding scheme for planning as satisfiability. In: AAAI, Atlanta, GA, pp. 89–94 (2010)
Keyder, E., Geffner, H.: Soft goals can be compiled away. JAIR 36, 547–556 (2009)
Knecht, M.: Efficient Domain-Independent Planning using Declarative Programming. Master’s thesis, University of Potsdam (December 2009)
Maratea, M.: An experimental evaluation of Max-SAT and PB solvers on over-subscription planning problems. In: RCRA, vol. 616 (2010)
Marques-Silva, J.: The MSUncore MaxSAT solver (2009)
Robinson, N., Gretton, C., Pham, D.N., Sattar, A.: Partial weighted MaxSAT for optimal planning. In: PRICAI, Daegu, Korea, pp. 231–243 (2010)
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Juma, F., Hsu, E.I., McIlraith, S.A. (2012). Preference-Based Planning via MaxSAT. In: Kosseim, L., Inkpen, D. (eds) Advances in Artificial Intelligence. Canadian AI 2012. Lecture Notes in Computer Science(), vol 7310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30353-1_10
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DOI: https://doi.org/10.1007/978-3-642-30353-1_10
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