Abstract
Planning as satisfiability is one of the main approaches to finding parallel optimal solution plans for classical planning problems. Existing high performance SAT-based planners are able to exploit either forward or backward search strategy; starting from an underestimation or overestimation of the optimal plan length, they keep increasing or decreasing the estimated plan length and, for each fixed length, they either find a solution or prove the unsatisfiability of the corresponding SAT instance.
In this paper we will discuss advantages and disadvantages of the underestimating and overestimating techniques, and we will propose an effective online decision system for selecting the most appropriate technique for solving a given planning problem. Finally, we will experimentally show that the exploitation of such a decision system improves the performance of the well known SAT-based planner SatPlan.
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References
Biere, A.: P{re,i}cosat@sc’09. In: SAT Competition 2009 (2009)
Blum, A., Furst, M.L.: Fast planning through planning graph analysis. Artificial Intelligence 90, 281–300 (1997)
Celorrio, S.J., Coles, A., Coles, A.: Learning track of the 7th International Planning Competition (2011), http://www.plg.inf.uc3m.es/ipc2011-learning
Davis, M., Logemann, G., Loveland, D.: A machine program for theorem-proving. Communications of the ACM 5(7), 394–397 (1962)
Gerevini, A.E., Saetti, A., Vallati, M.: Exploiting macro-actions and predicting plan length in planning as satisfiability. In: Pirrone, R., Sorbello, F. (eds.) AI*IA 2011. LNCS, vol. 6934, pp. 189–200. Springer, Heidelberg (2011)
Hall, M., Holmes, F.G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explorations 11(1), 10–18 (2009)
Hoffmann, J.: FF: The Fast-Forward Planning System. AI Magazine 22(3), 57–62 (2001)
Huang, R., Chen, Y., Zhang, W.: A novel transition based encoding scheme for planning as satisfiability. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI 2010), pp. 89–94. AAAI (2010)
Huang, R., Chen, Y., Zhang, W.: SAS+ planning as satisfiability. Journal of Artificial Intelligence Research 43, 293–328 (2012)
Kautz, H., Selman, B.: Planning as satisfiability. In: Proceedings of the 10th European Conference on Artificial Intelligence (ECAI 1992), pp. 359–363. John Wiley and Sons (1992)
Kautz, H., Selman, B.: Unifying sat-based and graph-based planning. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI 1999), pp. 318–325. Morgan Kaufmann (1999)
Kautz, H., Selman, B., Hoffmann, J.: SatPlan: Planning as satisfiability. In: Abstract Booklet of the 5th International Planning Competition (2006)
Mitchell, D., Selman, B., Levesque, H.: Hard and easy distributions of SAT problems. In: Proceedings of the 10th National Conference on Artificial Intelligence (AAAI 1992), pp. 459–465. AAAI (1992)
Nudelman, E., Leyton-Brown, K., Hoos, H.H., Devkar, A., Shoham, Y.: Understanding random SAT: Beyond the clauses-to-variables ratio. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 438–452. Springer, Heidelberg (2004)
Pfahringer, B., Holmes, G., Kirkby, R.: Optimizing the induction of alternating decision trees. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol. 2035, pp. 477–487. Springer, Heidelberg (2001)
Rintanen, J.: Engineering efficient planners with SAT. In: Proceedings of the 20th European Conference on Artificial Intelligence (ECAI 2012), pp. 684–689. IOS Press (2012)
Rintanen, J.: Evaluation strategies for planning as satisfiability. In: Proceedings of the 16th European Conference on Artificial Intelligence (ECAI 2004), pp. 682–687. IOS Press (2004)
Sideris, A., Dimopoulos, Y.: Constraint propagation in propositional planning. In: Proceedings of the 20th International Conference on Automated Planning and Scheduling (ICAPS 2010), pp. 153–160. AAAI (2010)
Sideris, A., Dimopoulos, Y.: Propositional planning as optimization. In: Proceedings of the 20th European Conference on Artificial Intelligence (ECAI 2012), pp. 732–737. IOS Press (2012)
Streeter, M.J., Smith, S.F.: Using decision procedures efficiently for optimization. In: Proceedings of the 17th International Conference on Automated Planning and Scheduling (ICAPS 2007), pp. 312–319. AAAI (2007)
Xing, Z., Chen, Y., Zhang, W.: MaxPlan: Optimal planning by decomposed satisfiability and backward reduction. In: Proceedings of the 5th International Planning Competition, IPC-5 (2006)
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Vallati, M., Chrpa, L., Crampton, A. (2013). Underestimation vs. Overestimation in SAT-Based Planning. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds) AI*IA 2013: Advances in Artificial Intelligence. AI*IA 2013. Lecture Notes in Computer Science(), vol 8249. Springer, Cham. https://doi.org/10.1007/978-3-319-03524-6_24
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DOI: https://doi.org/10.1007/978-3-319-03524-6_24
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