Avoiding game‐tree pathology in 2‐player adversarial search. (9th March 2018)

• Record Type: Journal Article
• Authors: Zuckerman, Inon; Wilson, Brandon; Nau, Dana S.
• Is Part Of: Computational intelligence. Volume 34:Number 2(2018)
• Page Start: 542
• Page End: 561
• Publication Date: 2018-03-09
• Subjects: adversarial search -- game playing -- game‐tree search -- 2‐player games; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12162
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Adversarial search, or game‐tree search, is a technique for analyzing an adversarial game to determine what moves a player should make in order to win a game. Until recently, lookahead pathology (in which 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 minimax algorithm. In addition, we provide an experimental evaluation on the African game of Kalah, which shows the improved performances of our suggested error‐minimizing minimax algorithm when there is a large degree of pathology.