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Computing Bayes-Nash Equilibria through Support Enumeration Methods in Bayesian Two-Player Strategic-Form Games

机译:计算贝叶斯纳什均衡通过贝叶斯二手战略 - 形式游戏的支持枚举方法

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The computation of equilibria in games is a challenging task. The literature studies the problem of finding Nash equilibria with complete-information games in depth, but not enough attention is paid to searching for equilibria in Bayesian games. Customarily, these games are reduced to complete information games and standard algorithms for computing Nash equilibria are employed. However, no work studied how these algorithms perform with Bayesian games. In this paper we focus on two-player strategic-form games. We show that the most efficient algorithm for computing Nash equilibria with GAMUT data (i.e., Porter-Nudelman-Shoham) is inefficient with Bayesian games, we provide an extension, and we experimentally evaluate its performance.
机译:游戏中均衡的计算是一个具有挑战性的任务。文献研究了通过完整信息游戏深入查找纳什均衡的问题,但没有足够的关注来寻找贝叶斯游戏的均衡。通常,这些游戏减少到完整的信息游戏和用于计算纳什均衡的标准算法。但是,没有工作研究这些算法如何与贝叶斯游戏进行。在本文中,我们专注于双人战略形式的游戏。我们表明,对于使用曲目数据(即,Porter-Nudelman-Shoham)计算Nash均衡的最有效算法与贝叶斯游戏效率低,我们提供了扩展,我们通过实验评估其性能。

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