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An opponent model is a set of definite opponent models.
Intuitively, an opponent model consists of all definite opponent models considered possible by the manipulator.
A definite opponent model is a pair ⟨Ф,∗⟩, where Ф is a belief set and ∗ is a belief revision operator.
An opponent model gives the initial beliefs of the believer, as well as the revision operator to be used.
First of all, by specifying an opponent model, we are able to make the assumptions about the beliefs of B explicit.
In order to evaluate the accuracy and performance of this proposed opponent model, it is compared with the state of the art models available in the Genius repository.
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There main concern is opponent modeling in the game.
The multi-model search framework generalizes minimax to allow exploitation of recursive opponent models.
Approaches to constructing exploitive agents are reviewed and the challenging problems of creating accurate and dynamic opponent models are addressed.
Opponent modeling is another difficult problem in decision-making applications, and it is essential to achieving high performance in poker.
Environments where agents may behave randomly, or using some other suboptimal strategy such as a human strategy, are being developed using opponent modeling [36 38].
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