Bibliography | |

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,” 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,” J.-Y. Audibert, R. Munos and C. Szepesvári, “Tuning Bandit Algorithms in Stochastic Environments,” J.-Y. Audibert, R. Munos and C. Szepesvari, “Exploration-exploitation 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 P. Auer, N. Cesa-Bianchi and P. Fischer, “Finite-Time Analysis of the Multiarmed Bandit Problem,” P. Auer, N. Cesa-Bianchi, Y. Freund and R. Schapire, “The non-stochastic multi-armed bandit problem,” P. Auer and R. Ortner, "Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning," A. Auger and O. Teytaud, “Continuous lunches are free plus the design of optimal optimization algorithms,” R.-K. Balla, R.-K. Balla and A. Fern, “UCT for tactical assault planning in real-time strategy games,” in V. Berthier, H. Doghmen and O. Teytaud, “Consistency Modiﬁcations for Automatically Tuned Monte-Carlo Tree Search,” 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,” Y. Björnsson and H. Finnsson, “CadiaPlayer: A Simulation-Based General game Player,” J. Borsboom, J.-T. Saito, G. Chaslot and J. Uiterwijk, “A Comparison of Monte-Carlo Methods for Phantom Go,” in A. Bourki, M. Coulm, P. Rolet, O. Teytaud and P. Vayssière, “Parameter Tuning by Simple Regret Algorithms and Multiple Simultaneous Hypothesis Testing,” B. Bouzy and B. Helmstetter, “Monte-Carlo Go Developments,” in B. Bouzy, "Associating domain-dependent knowledge and Monte Carlo approaches within a go program," in B. Bouzy, "Move Pruning Techniques for Monte-Carlo Go", in B. Bouzy and G. Chaslot, “Monte-Carlo Go Reinforcement Learning Experiments,” in B. Bouzy, "History and Territory Heuristics for Monte-Carlo Go," B. Bouzy, “Associating Shallow and Selective Global Tree Search with Monte Carlo for 9×9 Go,” J. Brodeur, B. Childs and L. Kocsis, “Transpositions and move groups in monte carlo tree search,” in C. Browne, B. Brugmann, S. Bubeck, R. Munos, G. Stoltz and C. Szepesvari, "Online Optimization in X-Armed Bandits," S. Bubeck, R. Munos and G. Stoltz, “Pure Exploration in Finitely-Armed and Continuously-Armed Bandits,” T. Cazenave and B. Helmstetter, "Combining Tactical Search and Monte-Carlo in the Game of Go," in T. Cazenave, “A Phantom-Go program,” in T. Cazenave, “Virtual Global Search: Application to 9×9 Go,” T. Cazenave, “Reflexive Monte-Carlo search,” in T. Cazenave and N. Jouandeau, “On the parallelization of UCT,” in T. Cazenave and N. Jouandeau, “A parallel Monte-Carlo tree search T. Cazenave, “Multi-player Go,” in T. Cazenave, “Monte-Carlo Kakuro,” in T. Cazenave, “Nested Monte Carlo Search,” in T. Cazenave, N. Jouandeau, “Parallel Nested Monte-Carlo Search,” in G. Chaslot, S. De Jong, J.-T. Saito and J. Uiterwijk, “Monte-Carlo Tree Search in Production Management Problems,” in G. Chaslot, J.-T. Saito, B. Bouzy, J. Uiterwijk and H. van den Herik, “Monte-Carlo Strategies for Computer Go,” in G. Chaslot, M. Winands, J. Uiterwijk, H. van den Herik and B. Bouzy, “Progressive Strategies for Monte-Carlo Tree Search,” in G. Chaslot, S. Bakkes, I. Szita and P. Spronck, “Monte-Carlo Tree Search: A New Framework for Game AI,” in G. Chaslot, M. Winands, I. Szita and H. van den Herik, “Cross-Entropy for Monte-Carlo Tree Search,” G. Chaslot, M. Winands and H. van den Herik, “Parallel Monte-Carlo Tree Search,” in G. Chaslot, M. Winands, J. Uiterwijk, H. van den Herik and B. Bouzy, “Progressive Strategies for Monte-Carlo Tree Search,” G. Chaslot, C. Fiter, J.-B. Hoock, A. Rimmel and O. Teytaud, “Adding Expert Knowledge and Exploration in Monte-Carlo Tree Search,” in G. Chaslot, J.-B. Hoock, J. Perez, A. Rimmel, O. Teytaud and M. Winands, “Meta Monte-Carlo Tree Search for Automatic Opening Book Generation,” 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,” 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,” 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,” K.-H. Chen, D. Du and P. Zhang, “Monte-Carlo Tree Search and Computer Go,” in M. Chung, M. Buro and J. Schaeffer, “Monte Carlo Planning in RTS Games,” in P. Ciancarini and G. Favini, “Monte Carlo Tree Search Techniques in the Game of Kriegspiel,” in Z. Clifford, P.-A. Coquelin and R. Munos, “Bandit Algorithms for Tree Search,” in R. Coulom, “Efficient selectivity and backup operators in Monte-Carlo 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 V. Demchik and A. Strelchenko, “Monte Carlo simulations on Graphics P. Drake and S. Uurtamo, “Move ordering vs heavy playouts: Where should heuristics be applied in Monte Carlo Go?” in M. Enzenberger and M. Müller, M. Enzenberger and M. Müller, “A Lock-free Multithreaded Monte-Carlo Tree Search Algorithm,” in E. Even-Dar, S. Mannor and Y. Mansour, “PAC bounds for multi-armed bandit and Markov decision processes,” in R. Farber, “CUDA, Supercomputing for the Masses: Part 1,” H. Finnsson, H. Finnsson and Y. Björnsson, “Simulation-Based Approach to General H. Finnsson and Y. Björnsson, “Simulation Control in General Game Playing Agents,” S. Gelly and Y. Wang, “Exploration exploitation in Go: UCT for Monte-Carlo Go,” S. Gelly, Y. Wang, R. Munos and O. Teytaud, S. Gelly and D. Silver, “Combining Online and Offline Knowledge in S. Gelly, S. Gelly and D. Silver, “Achieving Master Level Play in 9x9 Computer Go,” in S. Gelly, J.-B. Hoock, A. Rimmel, O. Teytaud and Y. Kalemkarian, “On the Parallelization of Monte-Carlo Planning,” E. Glassman and R. Tedrake, “A Quadratic Regulator-Based Heuristic for Rapidly Exploring State Space,” In C. Hartland, S. Gelly, N. Baskiotis, O. Teytaud, M. Sebag, “Multi-Armed Bandit, Dynamic Environments and Meta-Bandits,” C. Hartland, N. Baskiotis, S. Gelly, M. Sebag, O. Teytaud, “Change Point Detection and Meta-Bandits for Online Learning in Dynamic Environments,” D. Helmbold and A. Wood, “All Moves-As-First Heuristics in Monte-Carlo Go”, in J.-B. Hoock and O. Teytaud, “Bandit-Based Genetic Programming,” in H. Kato and I. Takeuchi, “Parallel Monte-Carlo Tree Search with simulation servers,” in M. Kearns, Y. Mansour and A. Ng, “A sparse sampling algorithm for near optimal planning in large Markovian decision processes,” in M. Kearns, Y. Mansour and A. Ng, “A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes,” R. Kleinberg, “Nearly tight bounds for the continuum-armed bandit problem,” in R. Kleinberg, A. Niculescu-Mizil and Y. Sharma. "Regret bounds for sleeping experts and bandits," L. Kocsis and C. Szepesvári, “Bandit based Monte-Carlo Planning,” in T. Kozelek, J. Langford and T. Zhang, "The Epoch-Greedy Algorithm for Contextual Multi-armed Bandits," S. LaValle, S. LaValle and J. Kuffner, “Rapidly-exploring random trees: Progress and prospects,” in S. LaValle, 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,” 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,” R. Lorentz, “Amazons Discover Monte-Carlo,” in O. Madani, D. Lizotte and R. Greiner, “The budgeted multi-armed bandit Problem,” in R. Maîtrepierre, J. Mary and R. Munos, "Adaptive play in Texas Hold'em Poker," in S. Mannor and J. N. Tsitsiklis, “The sample complexity of exploration in the multi-armed bandit problem,” S. Matsumoto, N. Hirosue, K. Itonaga, K. Yokoo and H. Futahashi, J. Maturana1, A. Fialho, F. Saubion, M. Schoenauer and M. Sebag, “Extreme Compass and Dynamic Multi-Armed Bandit for Adaptive Operator Selection,” B. McMahan and A. Blum, "Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary," M. Müller, R. Munos, H. Nakhost and M. Müller, “Monte-Carlo exploration for deterministic planning,” A. Ng, M. Kearns and Y. Mansour, “A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes,” J. Nijssen, J. Nijssen and J. Uiterwijk, “Using Intelligent Search Techniques to Play the Game Khet,” S. Pandey, D. Chakrabarti and D. Agrawal, "Multi-armed Bandit Problems with Dependent Arms," N. Pavlidis, D. Tasoulis, N. Adams and D. Hand, "Dynamic Multi-Armed Bandit with Covariates," in D. Pellier, B. Bouzy and M. Métivier, "An UCT Approach for Anytime Agent-based Planning", V. Podlozhnyuk, V. Podlozhnyuk and M. Harris, T. Raiko and J. Peltonen, “Application of UCT Search to the Connection Games of Hex, Y, *Star, and Renkula!” in A. Rimmel, A. Rimmel and F. Teytaud, “Multiple Overlapping Tiles for Contextual Monte Carlo Tree Search,” A. Rimmel, F. Teytaud and O. Teytaud, “Biasing Monte-Carlo Simulations through RAVE Values,” in A. Rimmel, O. Teytaud, C.-S. Lee, S.-J. Yen, M.-H. Wang and S.-R. Tsai, “Current Frontiers in Computer Go,” P. Rolet, M. Sebag and O. Teytaud, “Boosting Active Learning to Optimality: a Tractable Monte-Carlo, Billiard-based Algorithm,” P. Rolet, M. Sebag and O. Teytaud, “Upper Conﬁdence Trees and Billiards for Optimal Active Learning,” P. Rolet, M. Sebag and O. Teytaud, “Optimal active learning through billiards and upper confidence trees in continuous domains,” in P. Rolet and O. Teytaud, “Bandit-based Estimation of Distribution Algorithms for Noisy Optimization: Rigorous Runtime Analysis,” A. Saffadine, J.-T. Saito, G. Chaslot, J. Uiterwijk and H. van den Herik, “Pattern Knowledge for Proof-Number Search in Computer Go,” in J.-T. Saito, G. Chaslot, J. Uiterwijk and H. van den Herik, “Monte-Carlo Proof-Number Search,” in M. Schadd, M. Winands, H. van den Herik, G. Chaslot, and J. Uiterwijk, “Single-Player Monte-Carlo Tree Search,” in M. Schadd, M. Winands, H. van den Herik, G. Chaslot, and J. Uiterwijk, “Single-Player Monte-Carlo Tree Search,” in M. Schadd, M. Winands, H. van den Herik and H. Aldewereld, “Addressing NP-Complete Puzzles with Monte-Carlo Methods,” in J. Schäfer, M. Shafiei, N. Sturtevant and J. Schaeffer, “Comparing UCT versus CFR in Simultaneous Games,” S. Sharma, Z. Kobti and S. Goodwin, “Knowledge Generation for Improving Simulations in UCT for General Game Playing,” in 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 D. Silver and G. Tesauro, “Monte-Carlo Simulation Balancing,” in N. Sturtevant, “An Analysis of UCT in Multi-Player Games,” in I. Szita, G. Chaslot and P. Spronck, “Monte-Carlo Tree Search in Settlers of Catan,” in Y. Tanabe, K. Yoshizoe and H. Imai, “A study on security evaluation methodology for image-based biometrics authentication systems,” in S. Takeuchi, T. Kaneko and K. Yamaguchi, “Evaluation of Monte Carlo Tree Search and the Application to Go,” in G. Tesauro and G. Galperin, “On-line policy improvement using Monte-Carlo search,” O. Teytaud, S. Gelly and M. Sebag, “Anytime many-armed bandits,” F. Teytaud and O. Teytaud, “Creating an Upper-Confidence-Tree program for Havannah,” in F. Teytaud and O. Teytaud, “Du jeu de Go au Havannah: variantes d'UCT et coups décisifs,” D. Tom and M. Müller, “A Study of UCT and its Enhancements in an Artificial Game,” in S. Tomov, M. McGuigan, R. Bennett, G. Smith and J. Spiletic, “Benchmarking and Implementation of Probability-Based Simulations on Programmable Graphics Cards,” G. Trippen, “Plans, Patterns and Move Categories Guiding a Highly Selective Search,” in G. Van den Broeck, K. Driessens and J. Ramon, “Monte-Carlo Tree Search in Poker Using Expected Reward Distributions,” J. Vermorel and M. Mohri, “Multi-armed Bandit Algorithms and Empirical Evaluation,” T. Walsh, S. Goschin and M. Littman, “Integrating Sample-based Planning and Model-based Reinforcement Learning,” Y. Wang and S. Gelly, “Modifications of UCT and sequence-like simulations for Monte-Carlo Go,” in Y. Wang, J.-Y. Audibert and R. Munos, "Algorithms for Infinitely Many-Armed Bandits," C. Ward and P. Cowling, "Monte Carlo Search Applied to Card Selection in Magic: The Gathering", in G. Williams, M. Winands, Y. Björnsson and J.-T. Saito, “Monte-Carlo Tree Search Solver,” in M. Winands and Y. Björnsson, “Evaluation Functions Based Monte-Carlo LOA,” in F. Xie and Z. Liu, “Backpropagation Modification in Monte-Carlo Game Tree Search,” in J. Yang, Y. Gao, S. He, X. Liu, Y. Fu, Y. Chen and D. Ji, “To Create Intelligent Adaptive Game Opponent by Using Monte-Carlo for Tree Search,” in M. Zinkevich, M. Johanson, M. Bowling and C. Piccione, “Regret minimization in games with incomplete information,” in |