Pattern databases (PDBs) are memory-based abstraction heuristics that are constructed prior to the planning process which, if expressed symbolically, yield a very efficient representation. Recent work in the automatic generation of symbolic PDBs has established it as one of the most successful approaches for cost-optimal domain-independent planning. In this paper, we contribute two planners, both using bin-packing for its pattern selection. In the second one, we introduce a greedy selection algorithm called Partial-Gamer, which complements the heuristic given by bin-packing. We tested our approaches on the benchmarks of the last three International Planning Competitions, optimal track, getting very competitive results, with this simple and deterministic algorithm.
CITATION STYLE
Moraru, I., Edelkamp, S., Franco, S., & Martinez, M. (2019). Simplifying Automated Pattern Selection for Planning with Symbolic Pattern Databases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11793 LNAI, pp. 249–263). Springer Verlag. https://doi.org/10.1007/978-3-030-30179-8_21
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