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.
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Whereas DARPA has its “grand challenges”, ours are not so grand.
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The factor of 20 was chosen through limited empirical performance tuning. It is not necessarily optimal for this problem.
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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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