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

Nathan R. Sturtevant, Ariel Felner, Malte Helmert

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationProceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014
EditorsStefan Edelkamp, Roman Bartak
PublisherAAAI press
Pages175-183
Number of pages9
ISBN (Electronic)9781577356769
StatePublished - 1 Jan 2014
Event7th Annual Symposium on Combinatorial Search, SoCS 2014 - Prague, Czech Republic
Duration: 15 Aug 201417 Aug 2014

Publication series

NameProceedings of the 7th Annual Symposium on Combinatorial Search, SoCS 2014
Volume2014-January

Conference

Conference7th Annual Symposium on Combinatorial Search, SoCS 2014
Country/TerritoryCzech Republic
CityPrague
Period15/08/1417/08/14

ASJC Scopus subject areas

  • Computer Networks and Communications

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