论文标题
Optimin实现超纳什性能
Optimin achieves super-Nash performance
论文作者
论文摘要
自1990年代以来，AI系统在“赢家”具有明确定义的主要零和游戏中取得了超人的性能。但是，大多数社交互动是混合动物游戏，其中衡量AI系统的性能是一项非平凡的任务。在本文中，我提出了一种名为Super-Nash性能的新型基准测试，以评估混合动力设置中AI系统的性能。我表明，一个称为Optimin的解决方案概念在每个N-Per-serag游戏中都能达到超级纳什的性能，即，对于每个NASH平衡，都有一个Optimin，每个玩家不仅可以接收到，而且即使其他玩家会单方面和盈利与Optimin的偏离。
Since the 1990s, AI systems have achieved superhuman performance in major zero-sum games where "winning" has an unambiguous definition. However, most social interactions are mixed-motive games, where measuring the performance of AI systems is a non-trivial task. In this paper, I propose a novel benchmark called super-Nash performance to assess the performance of AI systems in mixed-motive settings. I show that a solution concept called optimin achieves super-Nash performance in every n-person game, i.e., for every Nash equilibrium there exists an optimin where every player not only receives but also guarantees super-Nash payoffs even if the others deviate unilaterally and profitably from the optimin.
