In asymmetric information zero-sum games, one player has superior information about the game over the other. Asymmetric information games are particularly relevant for security problems, e.g., where an attacker knows its own skill set or alternatively a system administrator knows the state of its resources. In such settings, the informed player is faced with the tradeoff of exploiting superior information at the cost of revealing that information. This tradeoff is typically addressed through randomization, in an effort to keep the uninformed player informationally off balance. A lingering issue is the explicit computation of such strategies. This talk gives an overview of work for repeated and discounted games on computing suboptimal strategies for the informed player in repeated games as well as discounted stochastic games where state transitions are not affected by the uninformed player.
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