Simplifying Automated Pattern Selection for Planning with Symbolic Pattern Databases

Ionut Moraru, Stefan Edelkamp, Santiago Franco, Moisés Martínez

Abstract: 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.