Improving local decisions in adversarial search

Brandon Wilson, Inon Zuckerman, Austin Parker, Dana S. Nau

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

6 Scopus citations


Until recently, game-tree pathology (in which a deeper game-tree search results in worse play) has been thought to be quite rare. We provide an analysis that shows that every game should have some sections that are locally pathological, assuming that both players can potentially win the game. We also modify the minimax algorithm to recognize local pathologies in arbitrary games, and cut off search accordingly (shallower search is more effective than deeper search when local pathologies occur). We show experimentally that our modified search procedure avoids local pathologies and consequently provides improved performance, in terms of decision accuracy, when compared with the ordinary minimax algorithm.

Original languageEnglish
Title of host publicationECAI 2012 - 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France - Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstration
PublisherIOS Press BV
Number of pages6
ISBN (Print)9781614990970
StatePublished - 2012
Event20th European Conference on Artificial Intelligence, ECAI 2012 - Montpellier, France
Duration: 27 Aug 201231 Aug 2012

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314


Conference20th European Conference on Artificial Intelligence, ECAI 2012


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