Dynamic potential search – a new bounded suboptimal search algorithm

Daniel Gilon, Ariel Felner, Roni Stern

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

14 Scopus citations

Abstract

Potential Search (PS) is an algorithm that is designed to solve bounded cost search problems. In this paper, we modify PS to work within the framework of bounded suboptimal search and introduce Dynamic Potential Search (DPS). DPS uses the idea of PS but modifies the bound to be the product of the minimal f-value in OPEN and the required suboptimal bound. We study DPS and its attributes. We then experimentally compare DPS to WA* and to EES on a variety of domains and study parameters that affect the behavior of these algorithms. In general we show that in domains with unit edge costs (e.g., many standard benchmarks) DPS significantly outperforms WA* and EES but there are exceptions.

Original languageEnglish
Title of host publicationProceedings of the 9th Annual Symposium on Combinatorial Search, SoCS 2016
EditorsJorge A. Baier, Adi Botea
PublisherAAAI press
Pages36-44
Number of pages9
ISBN (Electronic)9781577357698
StatePublished - 1 Jan 2016
Event9th Annual Symposium on Combinatorial Search, SoCS 2016 - Tarrytown, United States
Duration: 6 Jul 20168 Jul 2016

Publication series

NameProceedings of the 9th Annual Symposium on Combinatorial Search, SoCS 2016
Volume2016-January

Conference

Conference9th Annual Symposium on Combinatorial Search, SoCS 2016
Country/TerritoryUnited States
CityTarrytown
Period6/07/168/07/16

ASJC Scopus subject areas

  • Computer Networks and Communications

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