首个在模糊战棋中达到超人类水平的AI，突破信息不完整下的博弈难题。
General search techniques without common knowledge for imperfect-information games, and application to superhuman Fog of War chess
- 设计新搜索算法，无需依赖常识知识进行推理
- 在模糊战棋中击败顶尖人类与前代AI，表现显著更强
- 适用于大规模信息不完整博弈，适合对弈系统研究者
自人工智能诞生以来，游戏一直是技术进展的重要衡量标准。不完美信息类国际象棋变体存在百年以上，具有极高挑战性，是数十载人工智能研究的核心。除常规计算外，这类游戏还需对信息收集、对手认知、信号传递等进行推理。其中最具代表性的变体——模糊战棋（Fog of War chess，又称暗棋）自德州扑克实现超人水平后，便成为不完美信息博弈求解的重大挑战。本文提出Obscuro，首个在模糊战棋中实现超人水平的AI，其在不完美信息博弈搜索方面取得关键进展，支持强大且可扩展的推理能力。实验表明，该模型在对抗前代最优AI及顶级人类玩家（包括世界冠军）时均显著占优。模糊战棋是目前已知信息不完整程度最高、回合制零和博弈中实现超人水平的最大规模游戏，也是首次成功应用不完美信息搜索的超大规模零和博弈。
原文摘要 · Abstract (English)
Since the advent of AI, games have served as progress benchmarks. Meanwhile, imperfect-information variants of chess have existed for over a century, present extreme challenges, and have been the focus of decades of AI research. Beyond calculation needed in regular chess, they require reasoning about information gathering, the opponent's knowledge, signaling, etc. The most popular variant, Fog of War (FoW) chess (a.k.a. dark chess), has been a major challenge problem in imperfect-information game solving since superhuman performance was reached in no-limit Texas hold'em poker. We present Obscuro, the first superhuman AI for FoW chess. It introduces advances to search in imperfect-information games, enabling strong, scalable reasoning. Experiments against the prior state-of-the-art AI and human players -- including the world's best -- show that Obscuro is significantly stronger. FoW chess is the largest (by amount of imperfect information) turn-based zero-sum game in which superhuman performance has been achieved and the largest zero-sum game in which imperfect-information search has been successfully applied.
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