论文标题

可观察到的完美平衡

Observable Perfect Equilibrium

论文作者

Ganzfried, Sam

论文摘要

尽管NASH平衡已成为中央游戏理论解决方案概念,但许多重要的游戏都包含了几种Nash Equilibria,我们必须确定如何在它们之间进行选择以创建真正的战略代理。已经提出并研究了几种NASH平衡完善概念,用于顺序不完美的信息游戏,最突出的是颤抖的手完美平衡,准完美的平衡,以及最近的单侧准完美平衡。这些概念对某些任意的小错误是可靠的,并且可以保证永远存在。但是,我们认为,这些都不是在不完美信息的顺序游戏中开发强大代理的正确概念。我们为广泛形式的游戏定义了一种新的平衡完善概念,称为可观察到的完美平衡,其中该解决方案在公开观察的动作概率中对颤抖(不一定要通过反对玩家可以观察到的所有动作概率)进行了稳健。可观察到的完美平衡正确地捕获了以下假设:在观察到的错误时,对手正在尽可能合理地发挥作用(而以前的解决方案概念没有)。我们证明,始终可以保证可观察到的完美平衡,并证明它导致了与无限制扑克中先前的广泛形式改进不同的解决方案。我们预计可观察到的完美平衡将是一个有用的平衡精炼概念,用于建模许多重要的不完美信息游戏,这是人工智能中感兴趣的游戏。

While Nash equilibrium has emerged as the central game-theoretic solution concept, many important games contain several Nash equilibria and we must determine how to select between them in order to create real strategic agents. Several Nash equilibrium refinement concepts have been proposed and studied for sequential imperfect-information games, the most prominent being trembling-hand perfect equilibrium, quasi-perfect equilibrium, and recently one-sided quasi-perfect equilibrium. These concepts are robust to certain arbitrarily small mistakes, and are guaranteed to always exist; however, we argue that neither of these is the correct concept for developing strong agents in sequential games of imperfect information. We define a new equilibrium refinement concept for extensive-form games called observable perfect equilibrium in which the solution is robust over trembles in publicly-observable action probabilities (not necessarily over all action probabilities that may not be observable by opposing players). Observable perfect equilibrium correctly captures the assumption that the opponent is playing as rationally as possible given mistakes that have been observed (while previous solution concepts do not). We prove that observable perfect equilibrium is always guaranteed to exist, and demonstrate that it leads to a different solution than the prior extensive-form refinements in no-limit poker. We expect observable perfect equilibrium to be a useful equilibrium refinement concept for modeling many important imperfect-information games of interest in artificial intelligence.

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