Online sparse bandit for card games

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7168 LNCS, Issue , 2012, Pages 295-305

  St Pierre, David L ^a   Louveaux, Quentin ^a   Teytaud, Olivier ^b,c  

^a UNIVERSITY OF LIÈGE   (Belgium)

^b UNIVERSITÉ PARIS SACLAY   (France)

^c NATIONAL UNIVERSITY OF TAINAN   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

CARD GAMES; MATRIX GAME; NASH EQUILIBRIA; OFFLINE;

ALGORITHMS; COMPUTER SOFTWARE; HUMAN COMPUTER INTERACTION;

GAME THEORY;

EID: 84865001113     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-31866-5_25     Document Type: Conference Paper

References (9)

1. Audibert, J.Y., Bubeck, S.: Minimax policies for adversarial and stochastic bandits. In: 22nd Annual Conference on Learning Theory (COLT), Montreal (June 2009)
2. Audibert, J.-Y., Bubeck, S.: Regret bounds and minimax policies under partial monitoring. Journal of Machine Learning Research (October 2010)
3. Auer, P., Cesa-Bianchi, N., Freund, Y., Schapire, R.E.: Gambling in a rigged casino: the adversarial multi-armed bandit problem. In: Proceedings of the 36th Annual Symposium on Foundations of Computer Science, pp. 322-331. IEEE Computer Society Press, Los Alamitos (1995)
4. Auger, D.: Multiple Tree for Partially Observable Monte-Carlo Tree Search. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcázar, A.I., Merelo, J.J., Neri, F., Preuss, M., Richter, H., Togelius, J., Yannakakis, G.N. (eds.) EvoApplications 2011, Part I. LNCS, vol. 6624, pp. 53-62. Springer, Heidelberg (2011)
5. Grigoriadis, M.D., Khachiyan, L.G.: A sublinear-time randomized approximation algorithm for matrix games. Operations Research Letters 18(2), 53-58 (1995)
6. Lai, T., Robbins, H.: Asymptotically efficient adaptive allocation rules. Advances in Applied Mathematics 6(1), 4-22 (1985)
7. Lanctot, M., Waugh, K., Zinkevich, M., Bowling, M.: Monte Carlo Sampling for Regret Minimization in Extensive Games. Advances in Neural Information Processing Systems 22, 1078-1086 (2009)
8. Ponsen, M., Lanctot, M., de Jong, S.: MCRNR: Fast Computing of Restricted Nash Responses by Means of Sampling. In: Proceedings of Interactive Decision Theory and Game Theory Workshop, AAAI 2010 (2010)
9. Teytaud, O., Flory, S.: Upper Confidence Trees with Short Term Partial Information. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcázar, A.I., Merelo, J.J., Neri, F., Preuss, M., Richter, H., Togelius, J., Yannakakis, G.N. (eds.) EvoApplications 2011, Part I. LNCS, vol. 6624, pp. 153-162. Springer, Heidelberg (2011)