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.

翻译：自人工智能诞生以来，游戏一直作为其进展的衡量基准。与此同时，非完美信息版本的象棋已存在一个多世纪，它们提出了极端挑战，并成为数十年人工智能研究的焦点。除了常规象棋所需的计算能力外，这些变体还需要对信息收集、对手知识、信号传递等进行推理。其中最流行的变体——战争迷雾象棋（又称暗棋）——自无限制德州扑克实现超人表现以来，一直是非完美信息博弈求解领域的核心挑战问题。我们提出了Obscuro，首个在战争迷雾象棋中实现超人水平的人工智能。它在非完美信息博弈搜索方面引入了创新，实现了强大且可扩展的推理能力。通过与先前最先进的人工智能及人类玩家（包括世界顶尖选手）的对抗实验表明，Obscuro具有显著优势。战争迷雾象棋是目前已实现超人表现的最大规模（按非完美信息量衡量）回合制零和博弈，也是成功应用非完美信息搜索的最大规模零和博弈。