In Multiagent systems there are several agents with cooperative or competitive goals. Here, we are especially interested in zero-sum games which contain exactly two players with fully opposite goals. We describe a method based on Maximum-Expected-Utility  principle that learns the ingenuity of the opponent based on the moves of the opponent through a game and exploits this knowledge to play better against that opponent. Then we demonstrate an application of proposed method in the popular board game of Connect-4. The results show that the proposed method is superior compared to previous methods for adversarial environments especially when there is not adequate training for appropriate adaptation against an opponent.
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