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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hoffmann, J., Gomes, C.P., Selman, B., Kautz, H.A.: Sat encodings of state-space reachability problems in numeric domains. In: Proc. IJCAI (2007)
Kautz, H.A.: Deconstructing planning as satisfiability. In: Proc. AAAI (2006)
Russell, R., Holden, S.: Handling goal utility dependencies in a satisfiability framework. In: Proc. ICAPS (2010)
Giunchiglia, E., Maratea, M.: Planning as satisfiability with preferences. In: Proc. ICAPS (2007)
Robinson, N., Gretton, C., Pham, D.N., Sattar, A.: Sat-based parallel planning using a split representation of actions. In: Proc. ICAPS (2009)
Streeter, M., Smith, S.: Using decision procedures efficiently for optimization. In: Proc. ICAPS (2007)
Rintanen, J.: Evaluation strategies for planning as satisfiability. In: Proc. ECAI (2004)
Kautz, H., Selman, B.: Unifying SAT-based and graph-based planning. In: Proc. IJCAI (1999)
Keyder, E., Geffner, H.: Soft goals can be compiled away. Journal of Artificial Intelligence Research 36(1) (2009)
Bylander, T.: The computational complexity of propositional strips planning. Artificial Intelligence 69, 165–204 (1994)
Argelic, J., Li, C.M., Manya, F., Planes, J.: The first and second max-sat evaluations. Journal on Satisfiability, Boolean Modeling and Computation 4, 251–278 (2008)
Fu, Z., Malik, S.: On solving the partial max-sat problem. In: Biere, A., Gomes, C.P. (eds.) SAT 2006. LNCS, vol. 4121, pp. 252–265. Springer, Heidelberg (2006)
Blum, A., Furst, M.: Fast planning through planning graph analysis. Artificial Intelligence (90), 281–300 (1997)
Rintanen, J.: Planning graphs and propositional clause learning. In: Proc. KR (2008)
Kautz, H., McAllester, D., Selman, B.: Encoding plans in propositional logic. In: Proc. KR (1996)
Pipatsrisawat, K., Darwiche, A.: Rsat 2.0: SAT solver description. Technical Report D–153, Automated Reasoning Group, Computer Science Department, UCLA (2007)
Moskewicz, M.W., Madigan, C.F., Zhao, Y., Zhang, L., Malik, S.: Chaff: Engineering an Efficient SAT Solver. In: Proc. DAC (2001)
Marques-Silva, J.P., Sakallah, K.A.: Grasp - a new search algorithm for satisfiability. In: Proc. ICCAD (1996)
Huang, J.: The effect of restarts on the efficiency of clause learning. In: Proc. IJCAI (2007)
Robinson, N., Gretton, C., Pham, D.N.: Co-plan: Combining SAT-based planning with forward-search. In: Proc. IPC-6 (2008)
Büttner, M., Rintanen, J.: Satisfiability planning with constraints on the number of actions. In: Proc. ICAPS (2005)
Wolfman, S.A., Weld, D.S.: The LPSAT engine and its application to resource planning. In: Proc. IJCAI (1999)
Shin, J.A., Davis, E.: Processes and continuous change in a sat-based planner. Artif. Intell. 166(1-2), 194–253 (2005)
Helmert, M., Domshlak, C.: Landmarks, critical paths and abstractions: What’s the difference anyway? In: Proc. ICAPS (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Robinson, N., Gretton, C., Pham, D.N., Sattar, A. (2010). Partial Weighted MaxSAT for Optimal Planning. In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-15246-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15245-0
Online ISBN: 978-3-642-15246-7
eBook Packages: Computer ScienceComputer Science (R0)