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Moravčík M, Schmid M, Burch N, et al. DeepStack: Expert-level artificial intelligence in heads-up no-limit poker. Science. 2017;356(6337):508-513doi: 10.1126/science.aam6960.
Moravčík, M., Schmid, M., Burch, N., Lisý, V., Morrill, D., Bard, N., Davis, T., Waugh, K., Johanson, M., & Bowling, M. (2017). DeepStack: Expert-level artificial intelligence in heads-up no-limit poker. Science (New York, N.Y.), 356(6337), 508-513. https://doi.org/10.1126/science.aam6960
Moravčík, Matej, et al. "DeepStack: Expert-level artificial intelligence in heads-up no-limit poker." Science (New York, N.Y.) vol. 356,6337 (2017): 508-513. doi: https://doi.org/10.1126/science.aam6960
Moravčík M, Schmid M, Burch N, Lisý V, Morrill D, Bard N, Davis T, Waugh K, Johanson M, Bowling M. DeepStack: Expert-level artificial intelligence in heads-up no-limit poker. Science. 2017 May 05;356(6337):508-513. doi: 10.1126/science.aam6960. Epub 2017 Mar 02. PMID: 28254783.
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Science. 2017 May 05;356(6337):508-513. doi: 10.1126/science.aam6960. Epub 2017 Mar 02.

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

Science (New York, N.Y.)

Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael Bowling

Affiliations

  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. [email protected].

PMID: 28254783 DOI: 10.1126/science.aam6960

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.

Copyright © 2017, American Association for the Advancement of Science.

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