A Study of UCT and Its Enhancements in an Artificial Game

  • David Tom
  • Martin Müller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6048)


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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  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    van den Herik, H.J., Xu, X., Ma, Z., Winands, M.H.M. (eds.): CG 2008. LNCS, vol. 5131. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  3. 3.
    Gelly, S., Wang, Y., Munos, R., Teytaud, O.: Modification of UCT with Patterns in Monte-Carlo Go, Technical Report RR-6062 (2006)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Lorentz, R.J.: Amazons Discover Monte-Carlo. In: [2], pp. 13–24Google Scholar
  6. 6.
    Schaeffer, J.: The History Heuristic and Alpha-Beta Search Enhancements in Practice. IEEE Trans. Pattern Anal. Mach. Intell. 11(11), 1203–1212 (1989)CrossRefGoogle Scholar
  7. 7.
    Brügmann, B.: Monte Carlo Go (March 1993) (unpublished manuscript),
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    Coulom, R.: Whole-History Rating: A Bayesian Rating System for Players of Time-Varying Strength. In: [2], pp. 113–124Google Scholar
  11. 11.
    Enzenberger, M., Müller, M.: Fuego (2008), (Retrieved December 22, 2008)
  12. 12.
    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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • David Tom
    • 1
  • Martin Müller
    • 1
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

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