Simplifying Automated Pattern Selection for Planning with Symbolic Pattern Databases

Ionut Moraru; Stefan Edelkamp; Santiago Franco; Moises Martinez

doi:10.1007/978-3-030-30179-8_21

Simplifying Automated Pattern Selection for Planning with Symbolic Pattern Databases

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

^*Corresponding author for this work

Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	KI 2019
Subtitle of host publication	Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings
Editors	Christoph Benzmüller, Heiner Stuckenschmidt
Publisher	Springer Verlag
Pages	249-263
Number of pages	15
ISBN (Print)	9783030301781
DOIs	https://doi.org/10.1007/978-3-030-30179-8_21
Publication status	Published - 1 Jan 2019
Event	42nd German Conference on Artificial Intelligence, KI 2019 - Kassel, Germany Duration: 23 Sept 2019 → 26 Sept 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11793 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	42nd German Conference on Artificial Intelligence, KI 2019
Country/Territory	Germany
City	Kassel
Period	23/09/2019 → 26/09/2019

Keywords

Bin packing
Cost-optimal planning
Heuristic search

Access to Document

10.1007/978-3-030-30179-8_21

Cite this

Moraru, I., Edelkamp, S., Franco, S., & Martinez, M. (2019). Simplifying Automated Pattern Selection for Planning with Symbolic Pattern Databases. In C. Benzmüller, & H. Stuckenschmidt (Eds.), KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings (pp. 249-263). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11793 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-30179-8_21

Moraru, Ionut ; Edelkamp, Stefan ; Franco, Santiago et al. / Simplifying Automated Pattern Selection for Planning with Symbolic Pattern Databases. KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings. editor / Christoph Benzmüller ; Heiner Stuckenschmidt. Springer Verlag, 2019. pp. 249-263 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Moraru, I , Edelkamp, S, Franco, S & Martinez, M 2019, Simplifying Automated Pattern Selection for Planning with Symbolic Pattern Databases. in C Benzmüller & H Stuckenschmidt (eds), KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11793 LNAI, Springer Verlag, pp. 249-263, 42nd German Conference on Artificial Intelligence, KI 2019, Kassel, Germany, 23/09/2019. https://doi.org/10.1007/978-3-030-30179-8_21

Simplifying Automated Pattern Selection for Planning with Symbolic Pattern Databases. / Moraru, Ionut ; Edelkamp, Stefan; Franco, Santiago et al.
KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings. ed. / Christoph Benzmüller; Heiner Stuckenschmidt. Springer Verlag, 2019. p. 249-263 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11793 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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T1 - Simplifying Automated Pattern Selection for Planning with Symbolic Pattern Databases

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AU - Edelkamp, Stefan

AU - Franco, Santiago

AU - Martinez, Moises

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N2 - 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.

AB - 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.

KW - Bin packing

KW - Cost-optimal planning

KW - Heuristic search

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Moraru I , Edelkamp S, Franco S, Martinez M. Simplifying Automated Pattern Selection for Planning with Symbolic Pattern Databases. In Benzmüller C, Stuckenschmidt H, editors, KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings. Springer Verlag. 2019. p. 249-263. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-30179-8_21

Simplifying Automated Pattern Selection for Planning with Symbolic Pattern Databases

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Keywords

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