Name
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 binpacking 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-30179-8_21 | ADVANCES IN ARTIFICIAL INTELLIGENCE, KI 2019 |
Keywords | DocType | Volume |
Heuristic search, Cost-optimal planning, Bin packing | Conference | 11793 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ionut Moraru | 1 | 0 | 0.34 |
| Stefan Edelkamp | 2 | 0 | 0.68 |
| Santiago Franco | 3 | 0 | 1.01 |
| Moises Martinez | 4 | 0 | 0.34 |