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Science. 2017 May 5;356(6337):508-513. doi: 10.1126/science.aam6960. Epub 2017 Mar 2.

DeepStack: Expert-level artificial intelligence in heads-up no-limit poker.

Author information

1
Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada.
2
Department of Applied Mathematics, Charles University, Prague, Czech Republic.
3
Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic.
4
Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. bowling@cs.ualberta.ca.

Abstract

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, the quintessential game of imperfect information, is a long-standing 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 strategies that are more difficult to exploit than prior approaches.

PMID:
28254783
DOI:
10.1126/science.aam6960

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