Bandit-based genetic programming

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

Volumn 6021 LNCS, Issue , 2010, Pages 268-277

  Hoock, Jean Baptiste ^a   Teytaud, Olivier ^a  

^a CNRS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER GAMES; GENETIC ALGORITHMS;

COMPUTER GO; MATHEMATICAL PROPERTIES; MONTE-CARLO TREE SEARCHES; WORLD RECORDS;

GENETIC PROGRAMMING;

EID: 77952300111     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-12148-7_23     Document Type: Conference Paper

References (25)

1. Audibert, J.-Y., Munos, R., Szepesvari, C.: Use of variance estimation in the multiarmed bandit problem. In: NIPS 2006 Workshop on On-line Trading of Exploration and Exploitation (2006)
2. Audouard, P., Chaslot, G., Hoock, J.-B., Perez, J., Rimmel, A., Teytaud, O.: Grid coevolution for adaptive simulations; application to the building of opening books in the game of go. In: Proceedings of EvoGames (2009)
3. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite time analysis of the multiarmed bandit problem. Machine Learning 47(2/3), 235-256 (2002)
4. Auger, A., Teytaud, O.: Continuous lunches are free plus the design of optimal optimization algorithms. Algorithmica (2009)
5. Bruegmann, B.: Monte carlo go (1993)
6. Cazenave, T., Jouandeau, N.: On the parallelization of UCT. In: Proceedings of CGW 2007, pp. 93-101 (2007)
7. Chaslot, G., Saito, J.-T., Bouzy, B., Uiterwijk, J.W.H.M., van den Herik, H.J.: Monte-Carlo Strategies for Computer Go. In: Schobbens, P.-Y., Vanhoof, W., Schwanen, G. (eds.) Proceedings of the 18th BeNeLux Conference on Artificial Intelligence, Namur, Belgium, pp. 83-91 (2006)
8. Chaslot, G., Winands, M., Uiterwijk, J., van den Herik, H., Bouzy, B.: Progressive strategies for monte-carlo tree search. In: Wang, P., et al. (eds.) Proceedings of the 10th Joint Conference on Information Sciences (JCIS 2007), pp. 655-661. World Scientific Publishing Co. Pte. Ltd., Singapore (2007)
9. Chaslot, G., Winands, M., van den Herik, H.: Parallel Monte-Carlo Tree Search. In: van den Herik, H.J., Xu, X., Ma, Z., Winands, M.H.M. (eds.) CG 2008. LNCS, vol.5131, pp. 60-71. Springer, Heidelberg (2008)
10. 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)
11. Coulom, R.: Computing elo ratings of move patterns in the game of go. In: Computer Games Workshop, Amsterdam, The Netherlands (2007)
12. Crasmaru, M.: On the complexity of Tsume-Go. In: van den Herik, H.J., Iida, H. (eds.) CG 1998. LNCS, vol.1558, pp. 222-231. Springer, Heidelberg (1999)
13. Gelly, S., Hoock, J.B., Rimmel, A., Teytaud, O., Kalemkarian, Y.: The parallelization of monte-carlo planning. In: Proceedings of the International Conference on Informatics in Control, Automation and Robotics ICINCO 2008, pp. 198-203 (2008) (To appear)
14. Gelly, S., Silver, D.: Combining online and offline knowledge in UCT. In: ICML 2007: Proceedings of the 24th international conference on Machine learning, pp. 273-280. ACM Press, New York (2007)
15. Holland, J.H.: Genetic algorithms and the optimal allocation of trials. SIAM J. Comput. 2(2), 88-105 (1973)
16. Kato, H., Takeuchi, I.: Parallel monte-carlo tree search with simulation servers. In: 13th Game Programming Workshop (GPW 2008) (November 2008)
17. Kocsis, L., Szepesvari, 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)
18. Koza, J.R.: Genetic Programming: On the Programming of Computers by means of Natural Evolution. MIT Press, Massachusetts (1992)
19. Lai, T., Robbins, H.: Asymptotically efficient adaptive allocation rules. Advances in Applied Mathematics 6, 4-22 (1985)
20. Lee, C.-S., Wang, M.-H., Chaslot, G., 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 (2009)
21. Lichtenstein, D., Sipser, M.: Go is polynomial-space hard. J. ACM 27(2), 393-401 (1980)
22. Mnih, V., Szepesv́ari, C., Audibert, J.-Y.: Empirical Bernstein stopping. In: ICML 2008: Proceedings of the 25th international conference on Machine learning, pp. 672-679. ACM, New York (2008)
23. Robson, J.M.: The complexity of go. In: IFIP Congress, pp. 413-417 (1983)
24. Rolet, P., Sebag, M., Teytaud, O.: Optimal active learning through billiards and upper confidence trees in continous domains. In: Proceedings of the ECML conference (2009)
25. Wang, Y., Gelly, S.: Modifications of UCT and sequence-like simulations for Monte- Carlo Go. In: IEEE Symposium on Computational Intelligence and Games, Honolulu, Hawaii, pp. 175-182 (2007)