2017/01/06 by Matej Moravčík, Martin Schmid, Neil Burch +7 · 3 voices · 814 citations
Computer Science · Economics, Econometrics and Finance · Mathematics · Psychology · #Algorithm #Artificial Intelligence in Games #Artificial intelligence #Cognitive science #Computation #Computer science #Exploit #Gambling Behavior and Treatments #Imperfect #Intuition #Limit (mathematics) #Machine learning #Mathematical economics #Mathematics #Perfect information #Psychology #Sports Analytics and Performance #cs.AI
paper · pdf · doi:10.1126/science.aam6960
published in Science 356(6337), 508-513 (American Association for the Advancement of Science)
arxiv created 2017/03/03 · openalex publication_date 2017/03/03 · arxiv updated 2017/03/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Artificial intelligence has seen several breakthroughs in recent years, with games often serving as milestones. A common feature of these games is that players have perfect information. Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence. We introduce DeepStack, an algorithm for imperfect information settings. It combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition that is automatically learned from self-play using deep learning. In a study involving 44,000 hands of poker, DeepStack defeated with statistical significance professional poker players in heads-up no-limit Texas hold'em. The approach is theoretically sound and is shown to produce more difficult to exploit strategies than prior approaches.