Science · 2017 · 559 citations · 18 references
ComputingComputer ScienceStrategyArtificial IntelligenceHuman PerformanceMachine LearningArtificial Intelligence In PerformanceGame PlayingGame-playing AgentsGameplay ExperienceLearning In GamesTwo-player GamesWinning Strategies
No-limit Texas hold'em is the most popular form of poker. Despite artificial intelligence (AI) successes in perfect-information games, the private information and massive game tree have made no-limit poker difficult to tackle. We present Libratus, an AI that, in a 120,000-hand competition, defeated four top human specialist professionals in heads-up no-limit Texas hold'em, the leading benchmark and long-standing challenge problem in imperfect-information game solving. Our game-theoretic approach features application-independent techniques: an algorithm for computing a blueprint for the overall strategy, an algorithm that fleshes out the details of the strategy for subgames that are reached during play, and a self-improver algorithm that fixes potential weaknesses that opponents have identified in the blueprint strategy.
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Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison et al. · Nature · 2016 · 15.8K citations
The Weighted Majority Algorithm
N. Littlestone, Manfred K. Warmuth · Information and Computation · 1994 · 2K citations
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Journal of the Franklin Institute · 1960 · 1.8K citations