On creating complementary pattern databases

Santiago Franco, Álvaro Torralba, Levi H.S. Lelis, Mike Barley

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

18 Citations (Scopus)

Abstract

A pattern database (PDB) for a planning task is a heuristic function in the form of a lookup table that contains optimal solution costs of a simplified version of the task. In this paper we introduce a method that sequentially creates multiple PDBs which are later combined into a single heuristic function. At a given iteration, our method uses estimates of the A running time to create a PDB that complements the strengths of the PDBs created in previous iterations. We evaluate our algorithm using explicit and symbolic PDBs. Our results show that the heuristics produced by our approach are able to outperform existing schemes, and that our method is able to create PDBs that complement the strengths of other existing heuristics such as a symbolic perimeter heuristic.

Original languageEnglish
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
EditorsCarles Sierra
Number of pages8
PublisherInternational Joint Conferences on Artificial Intelligence
Publication date2017
Pages4302-4309
ISBN (Electronic)9780999241103
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017

Conference

Conference26th International Joint Conference on Artificial Intelligence, IJCAI 2017
Country/TerritoryAustralia
CityMelbourne
Period19/08/201725/08/2017
SponsorAlibaba Group, Artificial Intelligence Journal, et al., Melbourne Convention Bureau, Victoria Government, Xiaoi
SeriesIJCAI International Joint Conference on Artificial Intelligence
Volume0
ISSN1045-0823

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