提出算法求解不完全信息博弈中的演化稳定策略，支持多玩家且可提前终止。
Computing Evolutionarily Stable Strategies in Imperfect-Information Games
- 基于对称完美回忆博弈结构，设计可扩展的算法求解演化稳定策略
- 在非退化游戏中能计算全部ESS，退化时仍能找出部分解
- 适用于癌症信号博弈等真实场景，适合博弈论与生物演化研究者
我们提出一种算法，用于计算对称的、具有完美回忆的不完全信息广义形式博弈中的演化稳定策略（ESS）。主算法针对双人博弈，同时说明如何扩展至多人博弈。该算法是正确的，在非退化博弈中可计算所有ESS，而在退化博弈中则能找到包含无限多对称纳什均衡的一部分ESS。算法可提前停止，以快速获取一个或多个ESS。我们在不完全信息癌症信号博弈及随机博弈上进行了实验，验证了其可扩展性。
原文摘要 · Abstract (English)
We present an algorithm for computing evolutionarily stable strategies (ESSs) in symmetric perfect-recall extensive-form games of imperfect information. Our main algorithm is for two-player games, and we describe how it can be extended to multiplayer games. The algorithm is sound and computes all ESSs in nondegenerate games and a subset of them in degenerate games which contain an infinite continuum of symmetric Nash equilibria. The algorithm can be stopped early to find one or more ESSs. We experiment on an imperfect-information cancer signaling game as well as random games to demonstrate scalability.
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