General search techniques without common knowledge for imperfect-information games, and application to superhuman Fog of War chess

Brian Hu Zhang, Tuomas Sandholm

Keywords: imperfect-informaton games, subgame solving, game theory

Abstract: 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 game in which imperfect-information search has been successfully applied.

Primary Area: reinforcement learning

Submission Number: 18214