Exploiting the rubik’s cube 12-edge pdb by combining partial pattern databases and bloom filters

Nathan R. Sturtevant; Ariel Felner; Malte Helmert

Exploiting the rubik’s cube 12-edge pdb by combining partial pattern databases and bloom filters

Nathan R. Sturtevant
, Ariel Felner
, Malte Helmert

University of Denver
Ben-Gurion University of the Negev
University of Basel

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

3 Scopus citations

Abstract

Pattern Databases (PDBs) are a common form of abstraction-based heuristic which are often compressed so that a large PDB can fit in memory. Partial Pattern Databases (PPDBs) achieve this by storing only layers of the PDB which are close to the goal. This paper studies the problem of how to best compress and use the 457 GB 12-edge Rubik’s cube PDB, suggesting a number of ways that Bloom filters can be used to effectively compress PPDBs. We then develop a theoretical model of the common min compression approach and our Bloom filters, showing that the original method of compressed PPDBs can never be better than min compression. We conclude with experimental results showing that Bloom filter compression of PPDBs provides superior performance to min compression in Rubik’s cube.

Original language	English
Title of host publication	Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014
Editors	Stefan Edelkamp, Roman Bartak
Publisher	AAAI press
Pages	175-183
Number of pages	9
ISBN (Electronic)	9781577356769
State	Published - 2014
Externally published	Yes
Event	7th Annual Symposium on Combinatorial Search, SoCS 2014 - Prague, Czech Republic Duration: 15 Aug 2014 → 17 Aug 2014

Publication series

Name	Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014
Volume	2014-January

Conference

Conference	7th Annual Symposium on Combinatorial Search, SoCS 2014
Country/Territory	Czech Republic
City	Prague
Period	15/08/14 → 17/08/14

Bibliographical note

Publisher Copyright:
© 2015 AAAI press. All Rights Reserved.

Funding

This research was partly supported by the Israel Science Foundation (ISF) under grant #417/13 to Ariel Felner. Computing resources were provided from machines used for the NSF I/UCRC on Safety, Security, and Rescue. This research was partly supported by the Israel Science Foundation (ISF) under grant #417/13 to Ariel Felner. This work was partly supported by the Swiss National Science Foundation (SNSF) as part of the project “Abstraction Heuristics for Planning and Combinatorial Search” (AHPACS). This work was partly supported by the Swiss National Science Foundation (SNSF) as part of the project “Abstraction Heuristics for Planning and Combinatorial Search” (AHPACS).

Funders	Funder number
National Science Foundation
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Israel Science Foundation	417/13

Cite this

Sturtevant, N. R., Felner, A., & Helmert, M. (2014). Exploiting the rubik’s cube 12-edge pdb by combining partial pattern databases and bloom filters. In S. Edelkamp, & R. Bartak (Eds.), Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014 (pp. 175-183). (Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014; Vol. 2014-January). AAAI press.

Sturtevant, Nathan R. ; Felner, Ariel ; Helmert, Malte. / Exploiting the rubik’s cube 12-edge pdb by combining partial pattern databases and bloom filters. Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014. editor / Stefan Edelkamp ; Roman Bartak. AAAI press, 2014. pp. 175-183 (Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014).

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title = "Exploiting the rubik{\textquoteright}s cube 12-edge pdb by combining partial pattern databases and bloom filters",

abstract = "Pattern Databases (PDBs) are a common form of abstraction-based heuristic which are often compressed so that a large PDB can fit in memory. Partial Pattern Databases (PPDBs) achieve this by storing only layers of the PDB which are close to the goal. This paper studies the problem of how to best compress and use the 457 GB 12-edge Rubik{\textquoteright}s cube PDB, suggesting a number of ways that Bloom filters can be used to effectively compress PPDBs. We then develop a theoretical model of the common min compression approach and our Bloom filters, showing that the original method of compressed PPDBs can never be better than min compression. We conclude with experimental results showing that Bloom filter compression of PPDBs provides superior performance to min compression in Rubik{\textquoteright}s cube.",

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series = "Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014",

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Sturtevant, NR, Felner, A & Helmert, M 2014, Exploiting the rubik’s cube 12-edge pdb by combining partial pattern databases and bloom filters. in S Edelkamp & R Bartak (eds), Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014. Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014, vol. 2014-January, AAAI press, pp. 175-183, 7th Annual Symposium on Combinatorial Search, SoCS 2014, Prague, Czech Republic, 15/08/14.

Exploiting the rubik’s cube 12-edge pdb by combining partial pattern databases and bloom filters. / Sturtevant, Nathan R.; Felner, Ariel; Helmert, Malte.
Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014. ed. / Stefan Edelkamp; Roman Bartak. AAAI press, 2014. p. 175-183 (Proceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014; Vol. 2014-January).

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

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AB - Pattern Databases (PDBs) are a common form of abstraction-based heuristic which are often compressed so that a large PDB can fit in memory. Partial Pattern Databases (PPDBs) achieve this by storing only layers of the PDB which are close to the goal. This paper studies the problem of how to best compress and use the 457 GB 12-edge Rubik’s cube PDB, suggesting a number of ways that Bloom filters can be used to effectively compress PPDBs. We then develop a theoretical model of the common min compression approach and our Bloom filters, showing that the original method of compressed PPDBs can never be better than min compression. We conclude with experimental results showing that Bloom filter compression of PPDBs provides superior performance to min compression in Rubik’s cube.

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Exploiting the rubik’s cube 12-edge pdb by combining partial pattern databases and bloom filters

Abstract

Publication series

Conference

Bibliographical note

Funding

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