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Most Important Fundamental Rule of Poker Strategy

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  • Sam Ganzfried
  • Max Chiswick

Abstract

Poker is a large complex game of imperfect information, which has been singled out as a major AI challenge problem. Recently there has been a series of breakthroughs culminating in agents that have successfully defeated the strongest human players in two-player no-limit Texas hold 'em. The strongest agents are based on algorithms for approximating Nash equilibrium strategies, which are stored in massive binary files and unintelligible to humans. A recent line of research has explored approaches for extrapolating knowledge from strong game-theoretic strategies that can be understood by humans. This would be useful when humans are the ultimate decision maker and allow humans to make better decisions from massive algorithmically-generated strategies. Using techniques from machine learning we have uncovered a new simple, fundamental rule of poker strategy that leads to a significant improvement in performance over the best prior rule and can also easily be applied by human players.

Suggested Citation

  • Sam Ganzfried & Max Chiswick, 2019. "Most Important Fundamental Rule of Poker Strategy," Papers 1906.09895, arXiv.org, revised Feb 2020.
    • Handle: RePEc:arx:papers:1906.09895
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    References listed on IDEAS

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    1. Sam Ganzfried & Farzana Yusuf, 2017. "Computing Human-Understandable Strategies: Deducing Fundamental Rules of Poker Strategy," Games, MDPI, vol. 8(4), pages 1-13, November.
    2. Koller, Daphne & Megiddo, Nimrod, 1992. "The complexity of two-person zero-sum games in extensive form," Games and Economic Behavior, Elsevier, vol. 4(4), pages 528-552, October.
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    Cited by:

    1. Sam Ganzfried, 2021. "Human strategic decision making in parametrized games," Papers 2104.14744, arXiv.org, revised Nov 2021.
    2. Sam Ganzfried, 2022. "Human Strategic Decision Making in Parametrized Games," Mathematics, MDPI, vol. 10(7), pages 1-23, April.

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