AAAI Publications, Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence

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Solving Hanabi: Estimating Hands by Opponent's Actions in Cooperative Game with Incomplete Information
Hirotaka Osawa

Last modified: 2015-04-01

Abstract


A unique behavior of humans is modifying one’s unobservable behavior based on the reaction of others for cooperation. We used a card game called Hanabi as an evaluation task of imitating human reflective intelligence with artificial intelligence. Hanabi is a cooperative card game with incomplete information. A player cooperates with an opponent in building several card sets constructed with the same color and ordered numbers. However, like a blind man's bluff, each player sees the cards of all other players except his/her own. Also, communication between players is restricted to information about the same numbers and colors, and the player is required to read his/his opponent's intention with the opponent's hand, estimate his/her cards with incomplete information, and play one of them for building a set. We compared human play with several simulated strategies. The results indicate that the strategy with feedbacks from simulated opponent's viewpoints achieves more score than other strategies.

Keywords


imperfect information game; self-recognition; cognitive model; multi-agent interaction

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