Abstract
Non-determinism arises naturally in many real-world applications of action planning. Strong plans for this type of problems can be found using AO* search guided by an appropriate heuristic function. Most domain-independent heuristics considered in this context so far are based on the idea of ignoring delete lists and do not properly take the non-determinism into account. Therefore, we investigate the applicability of pattern database (PDB) heuristics to non-deterministic planning. PDB heuristics have emerged as rather informative in a deterministic context. Our empirical results suggest that PDB heuristics can also perform reasonably well in non-deterministic planning. Additionally, we present a generalization of the pattern additivity criterion known from classical planning to the non-deterministic setting.
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References
Cimatti, A., Pistore, M., Roveri, M., Traverso, P.: Weak, strong, and strong cyclic planning via symbolic model checking. Artificial Intelligence 147(1–2), 35–84 (2003)
Edelkamp, S., Kissmann, P.: Solving fully-observable non-deterministic planning problems via translation into a general game. In: Proc. 32nd German Annual Conference on Artificial Intelligence, KI 2009 (2009)
Bryce, D., Kambhampati, S., Smith, D.E.: Planning graph heuristics for belief space search. Journal of Artificial Intelligence Research 26, 35–99 (2006)
Hoffmann, J., Brafman, R.I.: Contingent planning via heuristic forward search with implicit belief states. In: Proc. 15th International Conference on Automated Planning and Scheduling (ICAPS 2005), pp. 71–80 (2005)
Bercher, P., Mattmüller, R.: A planning graph heuristic for forward-chaining adversarial planning. In: Proc. 18th European Conference on Artificial Intelligence (ECAI 2008), pp. 921–922 (2008)
Hansen, E.A., Zilberstein, S.: LAO*: A heuristic search algorithm that finds solutions with loops. Artificial Intelligence 129(1–2), 35–62 (2001)
Nilsson, N.J.: Principles of Artificial Intelligence. Springer, Heidelberg (1980)
Bercher, P.: Anwendung von Pattern-Database-Heuristiken zum Lösen nichtdeterministischer Planungsprobleme. Diplomarbeit, Albert-Ludwigs-Universität Freiburg im Breisgau (2009)
Edelkamp, S.: Planning with pattern databases. In: Proc. 6th European Conference on Planning (ECP 2001), pp. 13–24 (2001)
Edelkamp, S.: Automated creation of pattern database search heuristics. In: Edelkamp, S., Lomuscio, A. (eds.) MoChArt IV. LNCS (LNAI), vol. 4428, pp. 35–50. Springer, Heidelberg (2007)
Haslum, P., Botea, A., Bonet, B., Helmert, M., Koenig, S.: Domain-independent construction of pattern database heuristics for cost-optimal planning. In: Proc. 22nd AAAI Conference on Artificial Intelligence (AAAI 2007), pp. 1007–1012 (2007)
Hoffmann, J., Nebel, B.: The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14, 253–302 (2001)
Rintanen, J.: Constructing conditional plans by a theorem-prover. Journal of Artificial Intelligence Research 10, 323–352 (1999)
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Bercher, P., Mattmüller, R. (2009). Solving Non-deterministic Planning Problems with Pattern Database Heuristics. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_8
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DOI: https://doi.org/10.1007/978-3-642-04617-9_8
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