Time Management for Monte-Carlo Tree Search in Go

  • Hendrik Baier
  • Mark H. M. Winands
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7168)


The dominant approach for programs playing the game of Go is nowadays Monte-Carlo Tree Search (MCTS). While MCTS allows for fine-grained time control, little has been published on time management for MCTS programs under tournament conditions. This paper investigates the effects that various time-management strategies have on the playing strength in Go. We consider strategies taken from the literature as well as newly proposed and improved ones. We investigate both semi-dynamic strategies that decide about time allocation for each search before it is started, and dynamic strategies that influence the duration of each move search while it is already running. In our experiments, two domain-independent enhanced strategies, EARLY-C and CLOSE-N, are tested; each of them provides a significant improvement over the state of the art.


Time Management Search Time Time Allocation Dynamic Strategy Good Move 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Althöfer, I., Donninger, C., Lorenz, U., Rottmann, V.: On Timing, Permanent Brain and Human Intervention. In: van den Herik, H.J., Herschberg, I.S., Uiterwijk, J.W.H.M. (eds.) Advances in Computer Chess, vol. 7, pp. 285–297. University of Limburg, Maastricht (1994)Google Scholar
  2. 2.
    Baier, H., Drake, P.: The Power of Forgetting: Improving the Last-Good-Reply Policy in Monte Carlo Go. IEEE Transactions on Computational Intelligence and AI in Games 2(4), 303–309 (2010)CrossRefGoogle Scholar
  3. 3.
    Baudiš, P.: MCTS with Information Sharing. Master’s thesis, Charles University, Prague, Czech Republic (2011)Google Scholar
  4. 4.
    Cohen, J.: A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20(1), 37–46 (1960)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Coulom, R.: Criticality: a Monte-Carlo Heuristic for Go Programs. University of Electro-Communications, Tokyo, Japan (2009), Invited talkGoogle Scholar
  7. 7.
    Donninger, C.: A la recherche du temps perdu: ’That was easy’. ICCA Journal 17(1), 31–35 (1994)Google Scholar
  8. 8.
    Drake, P.: et al.: Orego Go Program (2011),
  9. 9.
    Free Software Foundation: GNU Go 3.8 (2009),
  10. 10.
    Gelly, S., Silver, D.: Combining online and offline knowledge in UCT. In: Ghahramani, Z. (ed.) Proceedings of the Twenty-Fourth International Conference on Machine Learning (ICML 2007). ACM International Conference Proceeding Series, vol. 227, pp. 273–280. ACM (2007)Google Scholar
  11. 11.
    Gelly, S., Wang, Y., Munos, R., Teytaud, O.: Modification of UCT with Patterns in Monte-Carlo Go. Tech. rep., HAL - CCSd - CNRS (2006)Google Scholar
  12. 12.
    Greenblatt, R., Eastlake III, D., Crocker, S.D.: The Greenblatt Chess Program. In: Proceedings of the Fall Joint Computer Conference, pp. 801–810 (1967)Google Scholar
  13. 13.
    Huang, S.C., Coulom, R., Lin, S.S.: Time Management for Monte-Carlo Tree Search Applied to the Game of Go. In: International Conference on Technologies and Applications of Artificial Intelligence, pp. 462–466. IEEE Computer Society, Los Alamitos (2010)CrossRefGoogle Scholar
  14. 14.
    Hyatt, R.M.: Using Time Wisely. ICCA Journal 7(1), 4–9 (1984)Google Scholar
  15. 15.
    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
  16. 16.
    Kocsis, L., Uiterwijk, J.W.H.M., van den Herik, H.J.: Learning Time Allocation Using Neural Networks. In: Marsland, T.A., Frank, I. (eds.) CG 2001. LNCS, vol. 2063, pp. 170–185. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  17. 17.
    Lee, C.S., Wang, M.H., Chaslot, G.M.J.B., Hoock, J.B., Rimmel, A., Teytaud, O., Tsai, S.R., Hsu, S.C., Hong, T.P.: The Computational Intelligence of MoGo Revealed in Taiwan’s Computer Go Tournaments. IEEE Transactions on Computational Intelligence and AI in Games 1(1), 73–89 (2009)CrossRefGoogle Scholar
  18. 18.
    Lee, C.S., Müller, M., Teytaud, O.: Special Issue on Monte Carlo Techniques and Computer Go. IEEE Transactions on Computational Intelligence and AI in Games 2(4), 225–228 (2010)CrossRefGoogle Scholar
  19. 19.
    Markovitch, S., Sella, Y.: Learning of Resource Allocation Strategies for Game Playing. Computational Intelligence 12(1), 88–105 (1996)CrossRefGoogle Scholar
  20. 20.
    Pellegrino, S., Hubbard, A., Galbraith, J., Drake, P., Chen, Y.P.: Localizing Search in Monte-Carlo Go Using Statistical Covariance. ICGA Journal 32(3), 154–160 (2009)Google Scholar
  21. 21.
    Šolak, R., Vučković, V.: Time Management during a Chess Game. ICGA Journal 32(4), 206–220 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hendrik Baier
    • 1
  • Mark H. M. Winands
    • 1
  1. 1.Games and AI Group, Department of Knowledge EngineeringMaastricht UniversityMaastrichtThe Netherlands

Personalised recommendations