Overconfidence or paranoia? search in imperfect-information games

  • Authors:

  • Austin Parker;Dana Nau;V. S. Subrahmanian

  • Affiliations:

  • Department of Computer Science, University of Maryland, College Park, MD;Department of Computer Science, University of Maryland, College Park, MD;Department of Computer Science, University of Maryland, College Park, MD

  • Venue:
  • AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
  • Year:
  • 2006

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Abstract

We derive a recursive formula for expected utility values in imperfect- information game trees, and an imperfect-information game tree search algorithm based on it. The formula and algorithm are general enough to incorporate a wide variety of opponent models. We analyze two opponent models. The "paranoid" model is an information-set analog of the minimax rule used in perfect-information games. The "overconfident" model assumes the opponent moves randomly. Our experimental tests in the game of kriegspiel chess (an imperfect-information variant of chess) produced surprising results: (1) against each other, and against one of the kriegspiel algorithms presented at IJCAI-05, the overconfident model usually outperformed the paranoid model; (2) the performance of both models depended greatly on how well the model corresponded to the opponent's behavior. These results suggest that the usual assumption of perfect-information game tree search--that the opponent will choose the best possible move--isn't as useful in imperfect-information games.