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Monte Carlo Approaches to Parameterized Poker Squares

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 10068)

Abstract

Parameterized Poker Squares (PPS) is a generalization of Poker Squares where players must adapt to a point system supplied at play time and thus dynamically compute highly-varied strategies. Herein, we detail the top three performing AI players in a PPS research competition, all three of which make various use of Monte Carlo techniques.

Keywords

  • Point System
  • Faculty Mentor
  • Simulated Game
  • Monte Carlo Tree Search
  • Heuristic Calculation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-50935-8_3
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Fig. 1.
Fig. 2.

Notes

  1. 1.

    Whereas DARPA has its “grand challenges”, ours are not so grand.

  2. 2.

    http://www.boardgamegeek.com/boardgame/41215/poker-squares,

    http://cs.gettysburg.edu/~tneller/games/pokersquares.

  3. 3.

    http://www.boardgamegeek.com/geeklist/152237/sequential-placement-optimization-games.

  4. 4.

    http://cs.gettysburg.edu/~tneller/games/pokersquares/eaai/results/.

  5. 5.

    The factor of 20 was chosen through limited empirical performance tuning. It is not necessarily optimal for this problem.

References

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Correspondence to Todd W. Neller .

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Neller, T.W. et al. (2016). Monte Carlo Approaches to Parameterized Poker Squares. In: Plaat, A., Kosters, W., van den Herik, J. (eds) Computers and Games. CG 2016. Lecture Notes in Computer Science(), vol 10068. Springer, Cham. https://doi.org/10.1007/978-3-319-50935-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-50935-8_3

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