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- W4300543196 abstract "Zero-sum asymmetric games model decision making scenarios involving two competing players who have different information about the game being played. A particular case is that of nested information, where one (informed) player has superior information over the other (uninformed) player. This paper considers the case of nested information in repeated zero-sum games and studies the computation of strategies for both the informed and uninformed players for finite-horizon and discounted infinite-horizon nested information games. For finite-horizon settings, we exploit that for both players, the security strategy, and also the opponent's corresponding best response depend only on the informed player's history of actions. Using this property, we refine the sequence form, and formulate an LP computation of player strategies that is linear in the size of the uninformed player's action set. For the infinite-horizon discounted game, we construct LP formulations to compute the approximated security strategies for both players, and provide a bound on the performance difference between the approximated security strategies and the security strategies. Finally, we illustrate the results on a network interdiction game between an informed system administrator and uniformed intruder." @default.
- W4300543196 created "2022-10-03" @default.
- W4300543196 creator A5028973855 @default.
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- W4300543196 date "2017-03-06" @default.
- W4300543196 modified "2023-09-27" @default.
- W4300543196 title "Efficient Strategy Computation in Zero-Sum Asymmetric Repeated Games" @default.
- W4300543196 doi "https://doi.org/10.48550/arxiv.1703.01952" @default.
- W4300543196 hasPublicationYear "2017" @default.
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