Compressing pattern databases with learning

Mehdi Samadi, Maryam Siabani, Ariel Felner, Robert Holte

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

11 Scopus citations

Abstract

A pattern database (PDB) is a heuristic function implemented as a lookup table. It stores the lengths of optimal solutions for instances of subproblems. Most previous PDBs had a distinct entry in the table for each subproblem instance. In this paper we apply learning techniques to compress PDBs by using neural networks and decision trees thereby reducing the amount of memory needed. Experiments on the sliding tile puzzles and the TopSpin puzzle show that our compressed PDBs significantly outperforms both uncompressed PDBs as well as previous compressing methods. Our full compressing system reduced the size of memory needed by a factor of up to 63 at a cost of no more than a factor of 2 in the search effort.

Original languageEnglish
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Pages495-499
Number of pages5
ISBN (Print)978158603891
DOIs
StatePublished - 1 Jun 2008
Event18th European Conference on Artificial Intelligence, ECAI 2008 - Patras, Greece
Duration: 21 Jul 200825 Jul 2008

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume178
ISSN (Print)0922-6389

Conference

Conference18th European Conference on Artificial Intelligence, ECAI 2008
Country/TerritoryGreece
CityPatras
Period21/07/0825/07/08

ASJC Scopus subject areas

  • Artificial Intelligence

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