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A Survey on Game Theory Optimal Poker (2401.06168v1)

Published 2 Jan 2024 in cs.GT and cs.AI

Abstract: Poker is in the family of imperfect information games unlike other games such as chess, connect four, etc which are perfect information game instead. While many perfect information games have been solved, no non-trivial imperfect information game has been solved to date. This makes poker a great test bed for Artificial Intelligence research. In this paper we firstly compare Game theory optimal poker to Exploitative poker. Secondly, we discuss the intricacies of abstraction techniques, betting models, and specific strategies employed by successful poker bots like Tartanian[1] and Pluribus[6]. Thirdly, we also explore 2-player vs multi-player games and the limitations that come when playing with more players. Finally, this paper discusses the role of machine learning and theoretical approaches in developing winning strategies and suggests future directions for this rapidly evolving field.

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References (9)
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  5. D. Billings, N. Burch, A. Davidson, R. Holte, J. Schaeffer, T. Schauenberg, and D. Szafron “Approximating Game-Theoretic Optimal Strategies for Full-scale Poker”
  6. Noam Brown and Tuomas Sandholm “Superhuman AI for multiplayer poker”
  7. Sam Ganzfried and Tuomas Sandholm “Computing an Approximate Jam/Fold Equilibrium for 3-player No-Limit Texas Hold’em Tournaments”
  8. X. Chen, X. Deng, S.-H. Teng, “Settling the complexity of computing two-player Nash equilibria”
  9. Billings, Darse, Denis Papp, Jonathan Schaeffer and Duane Szafron. “Opponent Modeling in Poker.” AAAI/IAAI (1998).

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