Simplifying Automated Pattern Selection for Planning with Symbolic Pattern Databases

Ionut Moraru, Stefan Edelkamp*, Santiago Franco, Moises Martinez

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationKI 2019
Subtitle of host publicationAdvances in Artificial Intelligence - 42nd German Conference on AI, Proceedings
EditorsChristoph Benzmüller, Heiner Stuckenschmidt
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783030301781
Publication statusPublished - 1 Jan 2019
Event42nd German Conference on Artificial Intelligence, KI 2019 - Kassel, Germany
Duration: 23 Sept 201926 Sept 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11793 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference42nd German Conference on Artificial Intelligence, KI 2019


  • Bin packing
  • Cost-optimal planning
  • Heuristic search

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