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2016 | OriginalPaper | Chapter

Monte Carlo Approaches to Parameterized Poker Squares

Authors : Todd W. Neller, Zuozhi Yang, Colin M. Messinger, Calin Anton, Karo Castro-Wunsch, William Maga, Steven Bogaerts, Robert Arrington, Clay Langley

Published in: Computers and Games

Publisher: Springer International Publishing

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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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previous chapter Using Deep Convolutional Neural Networks in Monte Carlo Tree Search

next chapter Monte Carlo Tree Search with Robust Exploration

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

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

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

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

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

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

Arrington, R., Langley, C., Bogaerts, S.: Using domain knowledge to improve monte-carlo tree search performance in parameterized poker squares. In: Proceedings of the 30th National Conference on Artificial Intelligence (AAAI 2016), pp. 4065–4070. AAAI Press, Menlo Park (2016)

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Castro-Wunsch, K., Maga, W., Anton, C.: Beemo, a Monte Carlo simulation agent for playing parameterized poker squares. In: Proceedings of the 30th National Conference on Artificial Intelligence (AAAI 2016), pp. 4071–4074. AAAI Press, Menlo Park (2016)

Furtak, T., Buro, M.: Recursive Monte Carlo search for imperfect information games. In: 2013 IEEE Conference on Computational Inteligence in Games (CIG), Niagara Falls, ON, Canada, 11–13 August 2013, pp. 1–8 (2013). http://dx.doi.org/10.1109/CIG.2013.6633646

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Neller, T.W., Messinger, C.M., Zuozhi, Y.: Learning and using hand abstraction values for parameterized poker squares. In: Proceedings of the 30th National Conference on Artificial Intelligence (AAAI 2016), pp. 4095–4100. AAAI Press, Menlo Park (2016)

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Title: Monte Carlo Approaches to Parameterized Poker Squares
Authors: Todd W. Neller
Zuozhi Yang
Colin M. Messinger
Calin Anton
Karo Castro-Wunsch
William Maga
Steven Bogaerts
Robert Arrington
Clay Langley
Publisher: Springer International Publishing
Book: Computers and Games
Print ISBN: 978-3-319-50934-1

Electronic ISBN: 978-3-319-50935-8

Copyright Year: 2016
DOI: https://doi.org/10.1007/978-3-319-50935-8_3

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