Unlike perfect-information games, imperfect-information games cannot be solved by decomposing the game into subgames that are solved independently. Instead, all decisions must consider the strategy of the game as a whole, and more computationally intensive algorithms are used. While it is not possible to solve an imperfect-information game exactly through decomposition, it is possible to approximate solutions, or improve existing strategies, by solving disjoint subgames. This process is referred to as subgame solving. We introduce subgame solving techniques that outperform prior methods both in theory and practice. We also show how to adapt them, and past subgame solving techniques, to respond to opponent actions that are outside the original action abstraction; this significantly outperforms the prior state-of-the-art approach, action translation. Finally, we show that subgame solving can be repeated as the game progresses down the tree, leading to lower exploitability. Subgame solving is a key component of Libratus, the first AI to defeat top humans in heads-up no-limit Texas hold'em poker.

介绍了在不完全信息博弈中如何使用子游戏求解技术，这些技术可以适应对手的行动并改善游戏过程中的解，被用于 Libratus，它是第一个在无限制德州扑克中击败顶级人类的 AI。

不完美信息博弈中的安全和嵌套子游戏求解