Avoiding game-tree pathology in 2-player adversarial search

Inon Zuckerman, Brandon Wilson, Dana S. Nau

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish
Pages (from-to)542-561
Number of pages20
JournalComputational Intelligence
Issue number2
StatePublished - May 2018


  • 2-player games
  • adversarial search
  • game playing
  • game-tree search


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