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This bibliography contains all known publications related to MCTS (155 at last count). Additional material such as presentations, web pages, repositories and so on may be found in the Resources page.

Please submit new entries to us with complete reference details, preferably in IEEE journal format (shown) with an associated URL.


B. Arneson, R. Hayward and P. Henderson, “MoHex wins Hex tournament,” ICGA Journal, 32(2), pp. 114-116, 2009.

B. Arneson, R. Hayward and P. Henderson, “Monte Carlo Tree Search Hex,” to be published, 2010.

J.-Y. Audibert, R. Munos and C. Szepesvari, “Use of variance estimation in the multi-armed bandit problem,” NIPS Workshop on On-line Trading of Exploration and Exploitation, Vancouver, 2006.

J.-Y. Audibert, R. Munos and C. Szepesvári, “Tuning Bandit Algorithms in Stochastic Environments,” ALT, pp. 150-165, 2007.

J.-Y. Audibert, R. Munos and C. Szepesvari, “Exploration-exploitation
trade-off using variance estimates in multi-armed bandits,” Theoretical Computer Science, 410, pp. 1876–1902, 2009.

P. Audouard, G. Chaslot, J.-B. Hoock, A. Rimmel, J. Perez and O. Teytaud, “Grid coevolution for adaptive simulations; application to the building of opening books in the game of Go,” in EvoGames, LNCS 5484, Berlin: Springer, pp. 323-332, 2009.

P. Auer, N. Cesa-Bianchi and P. Fischer, “Finite-Time Analysis of the Multiarmed Bandit Problem,” Machine Learning, 47(2-3), pp. 235-256, 2002.

P. Auer, N. Cesa-Bianchi, Y. Freund and R. Schapire, “The non-stochastic multi-armed bandit problem,” SIAM Journal on Computing, 32(1), pp. 48–77, 2003.

P. Auer and R. Ortner, "Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning," NIPS, 2006.

A. Auger and O. Teytaud, “Continuous lunches are free plus the design of optimal optimization algorithms,” Algorithmica, 2009.

R.-K. Balla, UCT for Tactical Assault Battles in Real-Time Strategy Games, M.Sc. thesis, Oregon State University, Oregon, USA, 2009.

R.-K. Balla and A. Fern, “UCT for tactical assault planning in real-time strategy games,” in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2009.

V. Berthier, H. Doghmen and O. Teytaud, “Consistency Modifications for Automatically Tuned Monte-Carlo Tree Search,” Lion4, 2010.

D. Billings, A. Davidson, T. Schauenberg, N. Burch, M. Bowling, R. Holte, J. Schaeffer and D. Szafron, “Game-Tree Search with Adaptation in Stochastic Imperfect-Information Games,” Computer and Games, LNCS 3846/2006, Berlin: Springer, pp. 21-34, 2006.

Y. Björnsson and H. Finnsson, “CadiaPlayer: A Simulation-Based General game Player,” IEEE Transactions on Computational Intelligence and AI in Games, 1:1, pp. 4-16, 2009.

J. Borsboom, J.-T. Saito, G. Chaslot and J. Uiterwijk, “A Comparison of Monte-Carlo Methods for Phantom Go,” in Proceedings of The 19th Belgian-Dutch Conference on Artificial Intelligence, M. Dastani and E. de Jong, Eds., 2007.

A. Bourki, M. Coulm, P. Rolet, O. Teytaud and P. Vayssière, “Parameter Tuning by Simple Regret Algorithms and Multiple Simultaneous Hypothesis Testing,” ICINCO 2010, 2010.

B. Bouzy and B. Helmstetter, “Monte-Carlo Go Developments,” in Advances in Computer Games 10: Many Games, Many Challenges, H. van den Herik, H. Iida and E. Heinz, Eds., Kluwer Academic Publishers, Boston, MA, USA, pp. 159-174, 2003.

B. Bouzy, "Associating domain-dependent knowledge and Monte Carlo approaches within a go program," in Information Sciences, Heuristic Search and Computer Game Playing IV, K. Chen, Ed., vol. 175, num. 4, pp. 247-257, 2005.

B. Bouzy, "Move Pruning Techniques for Monte-Carlo Go", in Proceedings of the 11th International Conference Advances in Computer Game (ACG), Taipei, Taiwan, J. van den Herik, S.-C. Hsu, T.-S. Hsu and H. Donkers, Eds., LNCS 4250, pp. 104-119, 2005.

B. Bouzy and G. Chaslot, “Monte-Carlo Go Reinforcement Learning Experiments,” in IEEE 2006 Symposium on Computational Intelligence in Games, G. Kendall and S. Louis, Eds., Reno, USA, pp. 187-194, 2006.

B. Bouzy, "History and Territory Heuristics for Monte-Carlo Go," New Mathematics and Natural Computation, 2(2), pp. 1-8, 2006.

B. Bouzy, “Associating Shallow and Selective Global Tree Search with Monte Carlo for 9×9 Go,” CG 2004, LNCS 3846, H. van den Herik et al., Eds., pp. 67–80, 2006.

J. Brodeur, B. Childs and L. Kocsis, “Transpositions and move groups in monte carlo tree search,” in IEEE Symposium on Computational Intelligence and Games, pp. 389-395, 2008.

C. Browne, Automatic Generation and Evaluation of Recombination Games, Ph.D. thesis, Faculty of Information Technology, QUT, Brisbane, Australia, 2008.

B. Brugmann, Monte Carlo Go, Technical report, Physics Department, Syracuse University, New York, USA, 1993.

S. Bubeck, R. Munos, G. Stoltz and C. Szepesvari, "Online Optimization in X-Armed Bandits," Advances in Neural Information Processing Systems 21, pp. 201–208, 2008.

S. Bubeck, R. Munos and G. Stoltz, “Pure Exploration in Finitely-Armed and Continuously-Armed Bandits,” ALT, pp. 23-37, 2009.

T. Cazenave and B. Helmstetter, "Combining Tactical Search and Monte-Carlo in the Game of Go," in Proceedings of the IEEE Symposium on Computational Intelligence and Games, pp. 171-175, 2005.

T. Cazenave, “A Phantom-Go program,” in Advances in Computer Games (ACG 11), LNCS 4250:120–125, 2006.

T. Cazenave, “Virtual Global Search: Application to 9×9 Go,” CG 2006, LNCS 4630, H. van den Herik et al. Eds., pp. 62–71, 2007.

T. Cazenave, “Reflexive Monte-Carlo search,” in Proceedings of the CGW, pp. 165-173, 2007.

T. Cazenave and N. Jouandeau, “On the parallelization of UCT,” in Proceedings of the Comput. Games Workshop, pp. 93–101, 2007.

T. Cazenave and N. Jouandeau, “A parallel Monte-Carlo tree search
algorithm,” in Proceedings of the 6th International Conference on Computers and Games, LNCS 5131, pp. 72–80, 2008.

T. Cazenave, “Multi-player Go,” in Proceedings of the 6th International Conference on Computers and Games, LNCS 5131, pp. 50-59, 2008.

T. Cazenave, “Monte-Carlo Kakuro,” in Advances in Computer Games (ACG12), Pamplona Espagne, Berlin: Springer, 2009.

T. Cazenave, “Nested Monte Carlo Search,” in Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI09), San Francisco: Morgan Kaufman, pp. 456-461, 2009.

T. Cazenave, N. Jouandeau, “Parallel Nested Monte-Carlo Search,” in Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing (IPDPS), pp. 1-6, 2009.

G. Chaslot, S. De Jong, J.-T. Saito and J. Uiterwijk, “Monte-Carlo Tree Search in Production Management Problems,” in Proceedings of the 18th BeNeLux Conference on Artificial Intelligence, P.-Y. Schobbens, W. Vanhoof and G. Schwanen, Eds., pp. 91-98, 2006.

G. Chaslot, J.-T. Saito, B. Bouzy, J. Uiterwijk and H. van den Herik, “Monte-Carlo Strategies for Computer Go,” in Proceedings of the 18th BeNeLux Conference on Artificial Intelligence, University of Namur, pp. 83-90, 2006.

G. Chaslot, M. Winands, J. Uiterwijk, H. van den Herik and B. Bouzy, “Progressive Strategies for Monte-Carlo Tree Search,” in Proceedings of the 10th Joint Conference on Information Sciences (JCIS 2007), P. Wang et al., Eds., World Scientific Publishing, pp. 655-661, 2007.

G. Chaslot, S. Bakkes, I. Szita and P. Spronck, “Monte-Carlo Tree Search: A New Framework for Game AI,” in Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference, AAAI Press, Menlo Park, pp. 216-217, 2008.

G. Chaslot, M. Winands, I. Szita and H. van den Herik, “Cross-Entropy for Monte-Carlo Tree Search,” ICGA Journal, 31(3), pp. 145-156, 2008.

G. Chaslot, M. Winands and H. van den Herik, “Parallel Monte-Carlo Tree Search,” in Proceedings of the Conference on Computers and Games 2008 (CG 2008), LNCS 5131, H. van den Herik., X. Xu, Z. Ma and M. Winands, Eds., Berlin: Springer, pp. 60-71, 2008.

G. Chaslot, M. Winands, J. Uiterwijk, H. van den Herik and B. Bouzy, “Progressive Strategies for Monte-Carlo Tree Search,” New Mathematics and Natural Computation, 4(3), pp. 343-357, 2008.

G. Chaslot, C. Fiter, J.-B. Hoock, A. Rimmel and O. Teytaud, “Adding Expert Knowledge and Exploration in Monte-Carlo Tree Search,” in Advances in Computer Games (ACG12), Pamplona Espagne, Berlin: Springer, 2009.

G. Chaslot, J.-B. Hoock, J. Perez, A. Rimmel, O. Teytaud and M. Winands, “Meta Monte-Carlo Tree Search for Automatic Opening Book Generation,” GIGA 09, 2009.

G. Chaslot, J.-B. Hoock, F. Teytaud and O. Teytaud, “On the huge benefit of quasi-random mutations for multimodal optimization with application to grid-based tuning of neurocontrollers,” ESANN, 2009.

G. Chaslot, L. Chatriot, C. Fiter, S. Gelly, J.-B. Hoock, J. Perez, A.Rimmel and O. Teytaud, “Combining expert, offline, transient and online knowledge in Monte-Carlo exploration,” 20?? [Online]. Available: http://www.lri.fr/ teytaud/eg.pdf

L. Chatriot, S. Gelly, J.-B. Hoock, J. Pérez, A. Rimmel and O. Teytaud, “Introduction de connaissances expertes en Bandit-Based Monte-Carlo Planning avec application au Computer-Go,” JFPDA, 2008.

L. Chatriot, C. Fiter, G. Chaslot, S. Gelly, J.-B. Hoock, J. Perez, A. Rimmel and O. Teytaud, “Combiner connaissances expertes, hors-ligne, transientes et en ligne pour l'exploration Monte-Carlo,” Revue d'Intelligence Artificielle, 2008.

K.-H. Chen, D. Du and P. Zhang, “Monte-Carlo Tree Search and Computer Go,” in Studies in Computational Intelligence, Advances in Information and Intelligent Systems, Berlin: Springer, pp. 201-225, 2009.

M. Chung, M. Buro and J. Schaeffer, “Monte Carlo Planning in RTS Games,” inIEEE Symposium on Computational Intelligence and Games, 2005.

P. Ciancarini and G. Favini, “Monte Carlo Tree Search Techniques in the Game of Kriegspiel,” in Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09), pp. 474-479, 2009.

Z. Clifford, GPU-Accelerated program to play Go, project report, Applied Parallel Computing course, MIT, Massachussetts, 2009.

P.-A. Coquelin and R. Munos, “Bandit Algorithms for Tree Search,” in Proceedings of 23rd Conference on Uncertainty in Artificial Intelligence, Vancouver, Canada, pp. 67-74, 2007.

R. Coulom, “Efficient selectivity and backup operators in Monte-Carlo
tree search,” in Proceedings of the 5th International Conference on Computers and Games, 2006, pp. 72–83.

R. Coulom, “Criticality: a Monte-Carlo Heuristic for Go Programs,” invited talk at the University of Electro-Communications, Tokyo, Japan, 2009.

F. De Mesmay, A. Rimmel, Y. Voronenko and M. Puschel, “Bandit-Based Optimization on Graphs with Application to Library Performance Tuning,” in International Conference on Machine Learning, Montreal, Canada, 2009.

V. Demchik and A. Strelchenko, “Monte Carlo simulations on Graphics
Processing Units,” 2009 [Online]. Available: http://arxiv.org/abs/0903.3053
P. Drake and S. Uurtamo, “Heuristics in Monte Carlo Go,” in Proceedings of the 2007 International Conference on Artificial Intelligence, CSREA Press, 2007.

P. Drake and S. Uurtamo, “Move ordering vs heavy playouts: Where should heuristics be applied in Monte Carlo Go?” in Proceedings of the 3rd North American Game-On Conference, 2007.

M. Enzenberger and M. Müller, Fuego - an open-source framework for board games and Go engine based on Monte-Carlo tree search, Technical report TR 09-08, Dept. of Computing Science, University of Alberta, Edmonton, Canada, 2009.

M. Enzenberger and M. Müller, “A Lock-free Multithreaded Monte-Carlo Tree Search Algorithm,” in Advances in Computer Games (ACG12), Pamplona Espagne, Berlin: Springer, 2009.

E. Even-Dar, S. Mannor and Y. Mansour, “PAC bounds for multi-armed bandit and Markov decision processes,” in Proceedings of the 15th Annual Conference on Computational Learning Theory, pp. 255–270, 2002.

R. Farber, “CUDA, Supercomputing for the Masses: Part 1,” Dr. Dobb’s Journal, April 15, 2008 [Online]. Available: http://www.drdobbs.com/high-performance-computing/207200659

H. Finnsson, CADIA-Player: A General Game Playing Agent, M.Sc.
thesis, School of Computer Science, Reykjavík University, Reykjavík, Iceland, 2007.

H. Finnsson and Y. Björnsson, “Simulation-Based Approach to General
Game Playing,” in Proceedings of the 23rd AAAI Conference on Artificial Intelligence, D. Fox and C. Gomes, Eds., Chicago, AAAI Press, pp. 259–264, 2008.

H. Finnsson and Y. Björnsson, “Simulation Control in General Game Playing Agents,” GIGA 09, 2009.

S. Gelly and Y. Wang, “Exploration exploitation in Go: UCT for Monte-Carlo Go,” NIPS, 2006.

S. Gelly, Y. Wang, R. Munos and O. Teytaud, Modification of UCT with Patterns in Monte-Carlo Go, Technical report 6062, INRIA, Orsay Cedex, France, 2006.

S. Gelly and D. Silver, “Combining Online and Offline Knowledge in
UCT,” in Proceedings of the 24th International Conference on Machine Learning, Z. Ghahramani, Ed., vol. 227, pp. 273–280, 2007.

S. Gelly, A Contribution to Reinforcement Learning; Application to Computer-Go, Ph.D. thesis, University of Paris South, Paris, France, 2007.

S. Gelly and D. Silver, “Achieving Master Level Play in 9x9 Computer Go,” in Proceedings of the 23rd AAAI Conference on Artificial Intelligence, D. Fox and C. Gomes, Eds., Chicago, AAAI Press, pp. 1537-1540, 2008.

S. Gelly, J.-B. Hoock, A. Rimmel, O. Teytaud and Y. Kalemkarian, “On the Parallelization of Monte-Carlo Planning,” ICINCO, 2008.

E. Glassman and R. Tedrake, “A Quadratic Regulator-Based Heuristic for Rapidly Exploring State Space,” In Proceedings of the International Conference on Robotics and Automation (ICRA), 2010.

C. Hartland, S. Gelly, N. Baskiotis, O. Teytaud, M. Sebag, “Multi-Armed Bandit, Dynamic Environments and Meta-Bandits,” NIPS 2006 Workshop: On-line Trading of Exploration and Exploitation, 2006.

C. Hartland, N. Baskiotis, S. Gelly, M. Sebag, O. Teytaud, “Change Point Detection and Meta-Bandits for Online Learning in Dynamic Environments,” CAP 2007, French National Conference on Machine Learning, 2007.

D. Helmbold and A. Wood, “All Moves-As-First Heuristics in Monte-Carlo Go”, in Proceedings of the 2009 International Conference on Artificial Intelligence (IC-AI 2009), pp. 605-610, 2009.

J.-B. Hoock and O. Teytaud, “Bandit-Based Genetic Programming,” in13th European Conference on Genetic Programming, 2010.

H. Kato and I. Takeuchi, “Parallel Monte-Carlo Tree Search with simulation servers,” in 13th Game Programming Workshop (GPW-08), 2008.

M. Kearns, Y. Mansour and A. Ng, “A sparse sampling algorithm for near optimal planning in large Markovian decision processes,” in Proceedings of International Joint Conference on AI (IJCAI), pp. 1324–1331, 1999.

M. Kearns, Y. Mansour and A. Ng, “A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes,” Machine Learning, 49, pp. 193-208, 2002.

R. Kleinberg, “Nearly tight bounds for the continuum-armed bandit problem,” in 18th Advances in Neural Information Processing Systems, 2004.

R. Kleinberg, A. Niculescu-Mizil and Y. Sharma. "Regret bounds for sleeping experts and bandits," COLT, 2008.

L. Kocsis and C. Szepesvári, “Bandit based Monte-Carlo Planning,” in 15th European Conference on Machine Learning (ECML 2006), pp. 282-293, 2006.

T. Kozelek, Methods of MCTS and the game Arimaa, M.Sc. thesis, Charles University in Prague, Prague, Czech Republic, 2009.

J. Langford and T. Zhang, "The Epoch-Greedy Algorithm for Contextual Multi-armed Bandits," NIPS, 2007.

S. LaValle, Rapidly-exploring random trees: A new tool for path planning, Technical report TR 98-11, Computer Science Dept., Iowa State University, October, 1998.

S. LaValle and J. Kuffner, “Rapidly-exploring random trees: Progress and prospects,” in Algorithmic and Computational Robotics: New Directions, B. R. Donald, K. Lynch and D. Rus, Eds., Wellesley: A K Peters, pp. 293-308, 2001.

S. LaValle, Planning Algorithms, Cambridge: Cambridge University Press, 2006.

C.-S. Lee, W. Mei-Hui, T.-P. Hong, G. Chaslot, J.-B. Hoock, A. Rimmel, O. Teytaud and Y.-H. Kuo, “A Novel Ontology for Computer Go Knowledge Management,” IEEE FUZZ, 2009.

C.-S. Lee, M.-H. Wang, G. Chaslot, J.-B. Hoock, A. Rimmel, O. Teytaud, S.-R. Tsai, S.-C. Hsu and T.-P. Hong, “The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments,” IEEE Transactions on Computational Intelligence and AI in Games, 1:1, pp. 73-89, 2009.

R. Lorentz, “Amazons Discover Monte-Carlo,” in CG ’08: Proceedings of the 6th International Conference on Computers and Games, H. van den Herik, Xinhe X., Zongmin M. and M. H. M. Winands, Eds., Berlin: Springer, pp. 13-24, 2008.

O. Madani, D. Lizotte and R. Greiner, “The budgeted multi-armed bandit Problem,” in Proceedings of the 17th Annual Conference on Computational Learning Theory, pp. 643–645, 2004.

R. Maîtrepierre, J. Mary and R. Munos, "Adaptive play in Texas Hold'em Poker," in Proceeding of ECAI 2008: 18th European Conference on Artificial Intelligence, pp. 458-462, 2008.

S. Mannor and J. N. Tsitsiklis, “The sample complexity of exploration in the multi-armed bandit problem,” Journal of Machine Learning Research, 5, pp. 623–648, 2004.

S. Matsumoto, N. Hirosue, K. Itonaga, K. Yokoo and H. Futahashi,
“Evaluation of Simulation Strategy on Single-Player Monte-Carlo Tree Search and its Discussion for a Practical Scheduling Problem,” in Proceedings of the International MultiConference of Engineers and Computer Scientists 2010, Vol. III (IMECS 2010), Hong Kong, 2010.

J. Maturana1, A. Fialho, F. Saubion, M. Schoenauer and M. Sebag, “Extreme Compass and Dynamic Multi-Armed Bandit for Adaptive Operator Selection,” CEC09, 2009.

B. McMahan and A. Blum, "Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary," COLT, 2004.

M. Müller, Fuego at the Computer Olympiad in Pamplona 2009: a tournament report, Technical report TR 09-09, Department of Computing Science, University of Alberta, Edmonton, Canada, 2009.

R. Munos, Hierarchical Bandits for Tree Search: Application to optimization and planning, Technical report, INRIA, 2008 [Online]. Available: http://sequel.futurs.inria.fr/munos/documents/Barbade.pdf

H. Nakhost and M. Müller, “Monte-Carlo exploration for deterministic planning,” IJCAI 2009, 2009.

A. Ng, M. Kearns and Y. Mansour, “A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes,” Machine Learning, 49:2-3, pp. 193-208, 2002.

J. Nijssen, Using Intelligent Search Techniques to Play the Game Khet, M.Sc. thesis, Maastricht University, Maastricht, The Netherlands, 2009.

J. Nijssen and J. Uiterwijk, “Using Intelligent Search Techniques to Play the Game Khet,” BNAIC, 2009.

S. Pandey, D. Chakrabarti and D. Agrawal, "Multi-armed Bandit Problems with Dependent Arms," ICML, 2006.

N. Pavlidis, D. Tasoulis, N. Adams and D. Hand, "Dynamic Multi-Armed Bandit with Covariates," in Proceeding of ECAI 2008: 18th European Conference on Artificial Intelligence, pp. 777-778, 2008.

D. Pellier, B. Bouzy and M. Métivier, "An UCT Approach for Anytime Agent-based Planning", PAAMS10, Salamanca, Spain, 2010.

V. Podlozhnyuk, Parallel Mersenne Twister, Technical report, NVIDIA, 2007.

V. Podlozhnyuk and M. Harris, Monte Carlo Option Pricing, Technical report, NVIDIA, 2008.

T. Raiko and J. Peltonen, “Application of UCT Search to the Connection Games of Hex, Y, *Star, and Renkula!” in Proceedings of the Finnish AI Conference (STeP 2008), Espoo, Finland, 2008.

A. Rimmel, Improvements and Evaluation of the Monte-Carlo Tree Search Algorithm, Ph.D. thesis, Laboratoire de Recherche en Informatique, France, Paris, 2009.

A. Rimmel and F. Teytaud, “Multiple Overlapping Tiles for Contextual Monte Carlo Tree Search,” Applications of Evolutionary Computing, LNCS 6024/2010, Berlin: Springer, pp. 201-210, 2010.

A. Rimmel, F. Teytaud and O. Teytaud, “Biasing Monte-Carlo Simulations through RAVE Values,” in The International Conference on Computers and Games 2010, 2010.

A. Rimmel, O. Teytaud, C.-S. Lee, S.-J. Yen, M.-H. Wang and S.-R. Tsai, “Current Frontiers in Computer Go,” IEEE Transactions on Computational Intelligence and AI in Games, under review, 2010.

P. Rolet, M. Sebag and O. Teytaud, “Boosting Active Learning to Optimality: a Tractable Monte-Carlo, Billiard-based Algorithm,” ECML, pp. 302-317, 2009.

P. Rolet, M. Sebag and O. Teytaud, “Upper Confidence Trees and Billiards for Optimal Active Learning,” CAP09, 2009.

P. Rolet, M. Sebag and O. Teytaud, “Optimal active learning through billiards and upper confidence trees in continuous domains,” in Proceedings of the European Conference on Machine Learning, 2009.

P. Rolet and O. Teytaud, “Bandit-based Estimation of Distribution Algorithms for Noisy Optimization: Rigorous Runtime Analysis,” Lion4, 2010.

A. Saffadine, Utilisation d’UCT au Hex, Technical report, Ecole Normale Superiure de Lyon, Lyon, France, 2008.

J.-T. Saito, G. Chaslot, J. Uiterwijk and H. van den Herik, “Pattern Knowledge for Proof-Number Search in Computer Go,” in Proceedings of the 18th BeNeLux Conference on Artificial Intelligence, G. Schwanen P-Y. Schobbens and W. Vanhoof, Eds., Namur, Belgium, pp. 275-281, 2006.

J.-T. Saito, G. Chaslot, J. Uiterwijk and H. van den Herik, “Monte-Carlo Proof-Number Search,” in Computers and Games, 2007.

M. Schadd, M. Winands, H. van den Herik, G. Chaslot, and J. Uiterwijk, “Single-Player Monte-Carlo Tree Search,” in Computers and Games, Proceedings of the 6th International Conference (CG2008), LNCS 5131, H. van den Herik, X. Xu, Z. Ma and M. Winands, Eds., Berlin: Springer, vol. 5131, pp. 1-12, 2008.

M. Schadd, M. Winands, H. van den Herik, G. Chaslot, and J. Uiterwijk, “Single-Player Monte-Carlo Tree Search,” in Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference, A. Nijholt, M. Pantic, M. Poel and H. Hondorp, Eds., pp. 361-362, 2008.

M. Schadd, M. Winands, H. van den Herik and H. Aldewereld, “Addressing NP-Complete Puzzles with Monte-Carlo Methods,” in Proceedings of the AISB 2008 Symposium on Logic and the Simulation of Interaction and Reasoning, vol. 9, pp.55-61, 2008.

J. Schäfer, The UCT Algorithm Applied to Games with Imperfect Information, Diploma thesis, Fakultat fur Informatik, Otto-von-Guericke-Universitat, Magdeburg, Germany, 2007.

M. Shafiei, N. Sturtevant and J. Schaeffer, “Comparing UCT versus CFR in Simultaneous Games,” GIGA 09, 2009.

S. Sharma, Z. Kobti and S. Goodwin, “Knowledge Generation for Improving Simulations in UCT for General Game Playing,” in AI 2008: Advances in Artificial Intelligence, Berlin: Springer, pp. 49-55, 2008.

A. Shkolnik, M. Levashov, I. Manchester and R. Tedrake, “Bounding on rough terrain with the littledog robot,” under review, 2010.

D. Silver, R. Sutton and M. Müller, “Sample-Based Learning and Search with Permanent and Transient Memories,” in Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), volume 307 of ACM International Conference Proceeding Series, W. Cohen, A. McCallum and S. Roweis, Eds., Helsinki, Finland, pp. 968-975, 2008.

D. Silver and G. Tesauro, “Monte-Carlo Simulation Balancing,” in ICML 2009, Danyluk et al., Eds., 2009.

N. Sturtevant, “An Analysis of UCT in Multi-Player Games,” in Computers and Games, Berlin: Heidelberg, pp. 37-49, 2008.

I. Szita, G. Chaslot and P. Spronck, “Monte-Carlo Tree Search in Settlers of Catan,” in Advances in Computer Games, Pamplona Espagne, Berlin: Springer, 2009.

Y. Tanabe, K. Yoshizoe and H. Imai, “A study on security evaluation methodology for image-based biometrics authentication systems,” in Proceedings of the 3rd IEEE International Conference on Biometrics: Theory, Applications and Systems, Washington, DC, USA, pp. 258-263, 2009.

S. Takeuchi, T. Kaneko and K. Yamaguchi, “Evaluation of Monte Carlo Tree Search and the Application to Go,” in IEEE Symposium on Computational Intelligence and Games (CIG '08), pp. 191-198, 2008.

G. Tesauro and G. Galperin, “On-line policy improvement using Monte-Carlo search,” Advances in Neural Information Processing 9, pp. 1068–1074, 1996.

O. Teytaud, S. Gelly and M. Sebag, “Anytime many-armed bandits,” CAP 2007, 2007.

F. Teytaud and O. Teytaud, “Creating an Upper-Confidence-Tree program for Havannah,” in Advances in Computer Games (ACG12), Pamplona Espagne, Berlin: Springer, pp. 65-74, 2009.

F. Teytaud and O. Teytaud, “Du jeu de Go au Havannah: variantes d'UCT et coups décisifs,” RFIA, 2010.

D. Tom and M. Müller, “A Study of UCT and its Enhancements in an Artificial Game,” in Advances in Computer Games (ACG12), Pamplona Espagne, Berlin: Springer, 2009.

S. Tomov, M. McGuigan, R. Bennett, G. Smith and J. Spiletic, “Benchmarking and Implementation of Probability-Based Simulations on Programmable Graphics Cards,” Computers & Graphics, Vol. 29, No. 1, Feb., pp. 71-80, 2005.

G. Trippen, “Plans, Patterns and Move Categories Guiding a Highly Selective Search,” in Advances in Computer Games (ACG12), Pamplona Espagne, Berlin: Springer, 2009.

G. Van den Broeck, K. Driessens and J. Ramon, “Monte-Carlo Tree Search in Poker Using Expected Reward Distributions,” ACML 2009, LNAI 5828, Z.-H. Zhou and T. Washio, Eds., pp. 367–381, 2009.

J. Vermorel and M. Mohri, “Multi-armed Bandit Algorithms and Empirical Evaluation,” ECML 2005, LNAI 3720, J. Gama et al. Eds., pp. 437–448, 2005.

T. Walsh, S. Goschin and M. Littman, “Integrating Sample-based Planning and Model-based Reinforcement Learning,” AAAI-10, 2010.

Y. Wang and S. Gelly, “Modifications of UCT and sequence-like simulations for Monte-Carlo Go,” in Proceedings of the 2007 IEEE Conference on Computational Intelligence and Games, pp. 175-182, 2007.

Y. Wang, J.-Y. Audibert and R. Munos, "Algorithms for Infinitely Many-Armed Bandits," NIPS, pp. 1729-1736, 2008.

C. Ward and P. Cowling, "Monte Carlo Search Applied to Card Selection in Magic: The Gathering", in IEEE Conference on Computational Intelligence in Games (CIG 2009), Milan, Italy, 2009.

G. Williams, Determining Game Quality Through UCT Tree Shape Anaysis, M.Sc. thesis, Department of Computing, Imperial College London, 2010.

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