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Opponent Modeling in Texas Hold’em Poker

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7654)

Abstract

In this paper a new algorithm for prediction opponent move in Texas Hold’em Poker game is presented. The algorithm is based on artificial intelligence approach – it uses several neural networks, each trained on a specific dataset. The results given by algorithm may be applied to improve players’ game. Moreover, the algorithm may be used as a part of more complex algorithm created for supporting decision making in Texas Hold’em Poker.

Keywords

  • Poker game
  • algorithm
  • artificial intelligence
  • neural network

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References

  1. Van der Kleij, A.A.J.: Monte Carlo Tree Search and Opponent Modeling through Player Clustering in no-limit Texas Hold’em Poker. University of Groningen, The Netherlands (2010)

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© 2012 Springer-Verlag Berlin Heidelberg

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Fedczyszyn, G., Koszalka, L., Pozniak-Koszalka, I. (2012). Opponent Modeling in Texas Hold’em Poker. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34707-8_19

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  • DOI: https://doi.org/10.1007/978-3-642-34707-8_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34706-1

  • Online ISBN: 978-3-642-34707-8

  • eBook Packages: Computer ScienceComputer Science (R0)