Abstract
Monte-Carlo tree search, especially the UCT algorithm and its enhancements, have become extremely popular. Because of the importance of this family of algorithms, a deeper understanding of when and how the different enhancements work is desirable. To avoid the hard to analyze intricacies of tournament-level programs in complex games, this work focuses on a simple abstract game, which is designed to be ideal for history-based heuristics such as RAVE. Experiments show the influence of game complexity and of enhancements on the performance of Monte-Carlo Tree Search.
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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)
van den Herik, H.J., Xu, X., Ma, Z., Winands, M.H.M. (eds.): CG 2008. LNCS, vol. 5131. Springer, Heidelberg (2008)
Gelly, S., Wang, Y., Munos, R., Teytaud, O.: Modification of UCT with Patterns in Monte-Carlo Go, Technical Report RR-6062 (2006)
Finnsson, H., Björnsson, Y.: Simulation-Based Approach to General Game Playing. In: Fox, D., Gomes, C.P. (eds.) AAAI, pp. 259–264. AAAI Press, Menlo Park (2008)
Lorentz, R.J.: Amazons Discover Monte-Carlo. In: [2], pp. 13–24
Schaeffer, J.: The History Heuristic and Alpha-Beta Search Enhancements in Practice. IEEE Trans. Pattern Anal. Mach. Intell. 11(11), 1203–1212 (1989)
Brügmann, B.: Monte Carlo Go (March 1993) (unpublished manuscript), http://www.cgl.ucsf.edu/go/Programs/Gobble.html
Gelly, S., Silver, D.: Combining Online and Offline Knowledge in UCT. In: Ghahramani, Z. (ed.) ICML. ACM International Conference Proceeding Series, vol. 227, pp. 273–280. ACM, New York (2007)
Bouzy, B., Helmstetter, B.: Monte-Carlo Go Developments. In: van den Herik, J., Iida, H., Heinz, E. (eds.) Advances in Computer Games. Many Games, Many Challenges. Proceedings of the ICGA / IFIP SG16 10th Advances in Computer Games Conference, pp. 159–174. Kluwer Academic Publishers, Dordrecht (2004)
Coulom, R.: Whole-History Rating: A Bayesian Rating System for Players of Time-Varying Strength. In: [2], pp. 113–124
Enzenberger, M., Müller, M.: Fuego (2008), http://fuego.sf.net/ (Retrieved December 22, 2008)
Smith, S.J.J., Nau, D.S.: An Analysis of Forward Pruning. In: AAAI 1994: Proceedings of the Twelfth National Conference on Artificial Intelligence, Menlo Park, CA, USA, vol. 2, pp. 1386–1391. American Association for Artificial Intelligence (1994)
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Tom, D., Müller, M. (2010). A Study of UCT and Its Enhancements in an Artificial Game. In: van den Herik, H.J., Spronck, P. (eds) Advances in Computer Games. ACG 2009. Lecture Notes in Computer Science, vol 6048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12993-3_6
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DOI: https://doi.org/10.1007/978-3-642-12993-3_6
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