Abstract
We propose an algorithm for computing approximate Nash equilibria of partially observable games using Monte-Carlo tree search based on recent bandit methods. We obtain experimental results for the game of phantom tic-tac-toe, showing that strong strategies can be efficiently computed by our algorithm.
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References
Audibert, J.Y., Bubeck, S.: Minimax policies for adversarial and stochastic bandits. In: Proceedings of the 22nd Annual Conference on Learning Theory. Omnipress (2009)
Auer, P., Cesa-Bianchi, N., Freund, Y., Schapire, R.E.: The nonstochastic multiarmed bandit problem. SIAM Journal on Computing 32(1), 48–77 (2003)
Borsboom, J., Saito, J., Chaslot, G., Uiterwijk, J.: A comparison of Monte-Carlo methods for Phantom Go. In: Proc. 19th Belgian–Dutch Conference on Artificial Intelligence–BNAIC, Utrecht, The Netherlands (2007)
Brown, G.W.: Iterative solution of games by fictitious play. Activity Analysis of Production and Allocation 13(1), 374–376 (1951)
Cazenave, T.: A Phantom-Go program. In: van den Herik, H.J., Hsu, S.-C., Hsu, T.-s., Donkers, H.H.L.M(J.) (eds.) CG 2005. LNCS, vol. 4250, pp. 120–125. Springer, Heidelberg (2006)
Cesa-Bianchi, N., Lugosi, G.: Prediction, learning, and games. Cambridge Univ Pr, Cambridge (2006)
Ciancarini, P., Favini, G.P.: Monte carlo tree search techniques in the game of Kriegspiel. In: Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI 2009), pp. 474–479 (2009)
Coulom, R.: Efficient selectivity and backup operators in monte-carlo tree search. In: van den Herik, H.J., Ciancarini, P., Donkers, H.H.L.M(J.) (eds.) CG 2006. LNCS, vol. 4630, pp. 72–83. Springer, Heidelberg (2007)
Fudenberg, D., Tirole, J.: Game Theory. MIT Press, Cambridge (1991)
Kocsis, L., Szepesvári, C.: Bandit based monte-carlo planning. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 282–293. Springer, Heidelberg (2006)
Li, D.H.: Kriegspiel: Chess Under Uncertainty. Premier Pub. Co. (1994)
Parker, A., Nau, D., Subrahmanian, V.S.: Game-tree search with combinatorially large belief states. In: International Joint Conference on Artificial Intelligence, vol. 19, p. 254 (2005)
Zinkevich, M., Johanson, M., Bowling, M., Piccione, C.: Regret minimization in games with incomplete information. Advances in Neural Information Processing Systems 20, 1729–1736 (2008)
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Auger, D. (2011). Multiple Tree for Partially Observable Monte-Carlo Tree Search. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20525-5_6
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DOI: https://doi.org/10.1007/978-3-642-20525-5_6
Publisher Name: Springer, Berlin, Heidelberg
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