Open Access
Issue
ESAIM: PS
Volume 23, 2019
Page(s) 176 - 216
DOI https://doi.org/10.1051/ps/2018006
Published online 01 May 2019
  1. B. Abramson, Expected-outcome: a general model of static evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 12 (1990) 182–193. [Google Scholar]
  2. P. Auer, N. Cesa-Bianchi and P. Fischer, Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47 (2002) 235–256. [Google Scholar]
  3. E.B. Baum and W.D. Smith, A Bayesian approach to relevance in game playing. Artif. Intell. 97 (1997) 195–242. [Google Scholar]
  4. C. Browne, E. Powley, D. Whitehouse, S. Lucas, P.I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis and S. Colton, A survey monte carlo tree search methods. IEEE Trans. Comput. Intell. AI Games 4 (2012) 1–43. [Google Scholar]
  5. S. Bubeck and N. Cesa-Bianchi, Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Found. Trends Mach. Learn. 5 (2012) 1–122. [CrossRef] [Google Scholar]
  6. L. Buşoniu, R. Munos and E. Páll, An analysis of optimistic, best-first search for minimax sequential decision making. IEEE Int. Symp. Approx. Dyn. Program. Reinf. Learn. (2014). [Google Scholar]
  7. G.M.J.B. Chaslot, M.H.M. Winands, H.J. van den Herik, J.W.H.M. Uiterwijk and B. Bouzy, Progressive strategies for Monte-Carlo tree search. New Math. Nat. Comput. 4 (2008) 343–357. [CrossRef] [Google Scholar]
  8. P.A. Coquelin and R. Munos, Bandit Algorithms for Tree Search. Technical report, INRIA RR-6141 (2007). [Google Scholar]
  9. R. Coulom, Efficient selectivity and backup operators in Monte-Carlo tree search, in Proc. of 5th Int. Conf. Comput. and Games, Turin, Italy (2006) 72–83. [Google Scholar]
  10. L. Devroye and O. Kamoun, Random minimax game trees, in Random Discrete Structures (Minneapolis, MN, 1993) Vol. 76 of The IMA Volumes in Mathematics and its Applications. Springer, New York (1996) 55–80. [CrossRef] [Google Scholar]
  11. A. Garivier, E. Kaufmann and W.M. Koolen, Maximin action identification: a new bandit framework for games, in JMLR: Workshop and Conference Proceedings, Vol. 49 (2016) 1–23. [Google Scholar]
  12. S. Gelly, Y. Wang, R. Munos and O. Teytaud, Modification of UCT With Patterns in Monte-Carlo Go. Tech. Rep. Inst. Nat. Rech. Inform. Auto. (INRIA), Paris (2006). [Google Scholar]
  13. M.L. Ginsberg, GIB: imperfect information in a computationally challenging game. J. Artif. Intell. Res. 14 (2001) 303–358. [Google Scholar]
  14. D. Golovin and A. Kraus, Adaptive submodularity: theory and applications in active learning and stochastic optimization. J. Artif. Intell. Res. 42 (2011) 427–486. [Google Scholar]
  15. L. Kocsisand C. Szepesvári, Bandit based Monte-Carlo planning, in Machine Learning: ECML, Vol. 4212 of Lecture Notes in Comput. Sci. Springer, Berlin (2006) 282–293. [CrossRef] [Google Scholar]
  16. C.S. Lee, M.H. Wang, G.M.J.B. Chaslot, J.B. Hoock, A. Rimmel, O. Teytaud et al., The computational intelligence of MoGo revealed in taiwans computer go tournaments. IEEE Trans. Comput. Intell. AI Games 1 (2009) 73–89. [Google Scholar]
  17. R. Munos, From bandits to Monte-Carlo tree search: the optimistic principle applied to optimization and planning, in Foundations and Trends in Machine Learning (Book 21). Now Publishers Inc. (2014) 146. [Google Scholar]
  18. J. Pearl, Asymptotic properties of minimax trees and game-searching procedures. Artif. Intell. 14 (1980) 113–138. [Google Scholar]
  19. B. Sheppard, World-championship-caliber srabble. Artif. Intell. 134 (2002) 241–275. [Google Scholar]
  20. D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, D. Hassabis, Mastering the game of go with deep neural networks and tree search. Nature 529 (2016) 484–489. [Google Scholar]
  21. M. Tarsi, Optimal search on some game trees. J. Assoc. Comput. Mach. 30 (1983) 389–396. [CrossRef] [Google Scholar]
  22. G. Tesauro, V.T. Rajan and R. Segal, Bayesian inference in Monte-Carlo tree search, in UAI’10 Proc. of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (2010) 580–588. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.