A Hybrid Approach to Parallelization of Monte Carlo Tree Search in General Game Playing

  • Maciej ŚwiechowskiEmail author
  • Jacek Mańdziuk
Part of the Studies in Computational Intelligence book series (SCI, volume 634)


In this paper, we investigate the concept of a parallelization of Monte Carlo Tree Search applied to games. Specifically, we consider General Game Playing framework, which has originated at Stanford University in 2005 and has become one of the most important realizations of the multi-game playing idea. We introduce a novel parallelization method, called Limited Hybrid Root-Tree Parallelization, based on a combination of two existing ones (Root and Tree Parallelization) additionally equipped with a mechanism of limiting actions available during the search process. The proposed approach is evaluated and compared to the non-limited hybrid version counterpart and to the Tree Parallelization method. The advantages over Root Parallelization are derived on a theoretical basis. In the experiments, the proposed method is more effective than Tree Parallelization and also than non-limited hybrid version in certain games.


Monte Carlo Tree Search Upper Confidence Bounds Applied for Trees General Game Playing Parallelization Parallel Computing 



M. Świechowski was supported by the Foundation for Polish Science under International Projects in Intelligent Computing (MPD) and The European Union within the Innovative Economy Operational Programme and European Regional Development Fund.

This research was financed by the National Science Centre in Poland, based on the decision DEC-2012/07/B/ST6/01527.


  1. 1.
    Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison-Wesley, Boston (1995)zbMATHGoogle Scholar
  2. 2.
    Arneson, B., Hayward, R.B., Henderson, P.: Monte Carlo tree search in hex. IEEE Trans. Comput. Intell. AI Games 2(4), 251–258 (2010)CrossRefGoogle Scholar
  3. 3.
    Björnsson, Y., Finnsson, H.: CadiaPlayer: a simulation-based general game player. IEEE Trans. Comput. Intell. AI Games 1(1), 4–15 (2009)CrossRefGoogle Scholar
  4. 4.
    Bratko, I.: Prolog Programming for Artificial Intelligence. International Computer Science Series. Addison Wesley, Boston (2001)zbMATHGoogle Scholar
  5. 5.
    Browne, C.B., Powley, E., Whitehouse, D., Lucas, S.M., Cowling, P.I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., Colton, S.: A survey of Monte Carlo tree search methods. IEEE Trans. Comput. Intell. AI Games 4(1), 1–43 (2012)CrossRefGoogle Scholar
  6. 6.
    Cazenave, T., Jouandeau, N.: On the parallelization of UCT. Proc. CGW07, 93–101 (2007)Google Scholar
  7. 7.
    Cazenave, T., Jouandeau, N.: A parallel Monte-Carlo tree search algorithm. In: Computers and Games, pp. 72–80. Springer, Berlin (2008)Google Scholar
  8. 8.
    Chaslot, G., Winands, M.H., Szita, I., van den Herik, H.J.: Cross-entropy for Monte-Carlo tree search. ICGA J. 31(3), 145–156 (2008)Google Scholar
  9. 9.
    Coulom, R.: Efficient selectivity and backup operators in Monte-Carlo tree search. In: Computers and Games, pp. 72–83. Springer, Berlin (2007)Google Scholar
  10. 10.
    Enzenberger, M., Müller, M.: A lock-free multithreaded Monte-Carlo tree search algorithm. In: Advances in Computer Games, pp. 14–20. Springer, Berlin (2010)Google Scholar
  11. 11.
    Gelly, S., Silver, D.: Achieving Master Level Play in 9 x 9 Computer Go. In: AAAI, vol. 8, pp. 1537–1540 (2008)Google Scholar
  12. 12.
    Gelly, S., Kocsis, L., Schoenauer, M., Sebag, M., Silver, D., Szepesvári, C., Teytaud, O.: The grand challenge of computer Go: Monte Carlo tree search and extensions. Commun. ACM 55(3), 106–113 (2012)CrossRefGoogle Scholar
  13. 13.
    Genesereth, M.R., Love, N., Pell, B.: General game playing: overview of the AAAI competition. AI Mag. 26(2), 62–72 (2005)Google Scholar
  14. 14.
    Kocsis, L., Szepesvári, C.: Bandit based Monte-Carlo planning. In: Proceedings of the 17th European Conference on Machine Learning. ECML’06, pp. 282–293. Springer, Berlin (2006)Google Scholar
  15. 15.
    Love, N., Hinrichs, T., Haley, D., Schkufza, E., Genesereth, M.: General Game Playing: Game Description Language specification. (2008)
  16. 16.
    Mańdziuk, J., Świechowski, M.: Generic heuristic approach to general game playing. In: Bieliková, M., Friedrich, G., Gottlob, G., Katzenbeisser, S., Turán, G. (eds.) SOFSEM. Lecture Notes in Computer Science, vol. 7147, pp. 649–660. Springer, Berlin (2012)Google Scholar
  17. 17.
    Méhat, J., Cazenave, T.: A parallel general game player. Künstliche Intell. 25(1), 43–47 (2011)CrossRefGoogle Scholar
  18. 18.
    Méhat, J., Cazenave, T.: Tree parallelization of ary on a cluster. In: Proceedings of the IJCAI-11 Workshop on General Game Playing (GIGA’11), pp. 39–43 (2011)Google Scholar
  19. 19.
    Plaat, A., Schaeffer, J., Pijls, W., de Bruin, A.: Best-first fixed-depth minimax algorithms. Artif. Intell. 87(1–2), 255–293 (1996)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Świechowski, M.: Adaptive simulation-based meta-heuristic methods in synchronous multiplayer games. Ph.D. thesis, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland (2015) in reviewGoogle Scholar
  21. 21.
    Świechowski, M., Mańdziuk, J.: Fast interpreter for logical reasoning in general game playing. J. Log. Comput. (2014). doi: 10.1093/logcom/exu058 Google Scholar
  22. 22.
    Świechowski, M., Mańdziuk, J.: Self-adaptation of playing strategies in general game playing. IEEE Trans. Comput. Intell. AI Games 6(4), 367–381 (2014)CrossRefGoogle Scholar
  23. 23.
    Świechowski, M., Mańdziuk, J.: Specialized vs. multi-game approaches to AI in games. In: Angelov, P., Atanassov, K., Doukovska, L., Hadjiski, M., Jotsov, V., Kacprzyk, J., Kasabov, N., Sotirov, S., Szmidt, E., Zadrożny, S. (eds.) Intelligent Systems’2014. Advances in Intelligent Systems and Computing, vol. 322, pp. 243–254. Springer International Publishing (2015)Google Scholar
  24. 24.
    Świechowski, M., Mańdziuk, J., Ong, Y.S.: Specialization of a UCT-based general game playing program to single-player games. IEEE Trans. Comput. Intell. AI Games (2015) (accepted for publication)Google Scholar
  25. 25.
    Świechowski, M., Park, H., Mańdziuk, J., Kim, K.: Recent advances in general game playing. Sci. World J. 2015. (2015)
  26. 26.
    Syed, O., Syed, A.: Arimaa - a new game designed to be difficult for computers. ICGA 26, 138–139 (2003)Google Scholar
  27. 27.
    Teytaud, F., Teytaud, O.: Creating an upper-confidence-tree program for Havannah. In: Advances in Computer Games, pp. 65–74. Springer, Berlin (2010)Google Scholar

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© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Ph.D. Studies at Systems Research Institute, Polish Academy of SciencesWarsawPoland
  2. 2.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland
  3. 3.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore

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