, Adaptive Playout Policies for Monte-Carlo Go ». Mém.de mast, Institut für Kognitionswissenschaft, 2010.
, Two Online Learning Playout Policies in Monte Carlo Go: An Application of Win/Loss States, IEEE Transactions on Computational Intelligence and AI in Games, vol.6, issue.1, pp.1-9, 2014.
DOI : 10.1109/TCIAIG.2013.2292565
, « Computer Go : An AI oriented survey, Artificial Intelligence, vol.1321, issue.23, pp.39-103, 2001.
, « Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems, Foundations and Trends® in Machine Learning, vol.51, pp.1-122, 2012.
, The Power of Forgetting: Improving the Last-Good-Reply Policy in Monte Carlo Go, IEEE Transactions on Computational Intelligence and AI in Games 2, pp.303-309, 2010.
DOI : 10.1109/TCIAIG.2010.2100396
, « A survey about Solitaire Clobber, pp.18-189, 2013.
, PACHI: State of the Art Open Source Go Program, Advances in Computer Games, pp.24-38, 2012.
DOI : 10.1007/978-3-642-31866-5_3
, « Monte Carlo go developments, Advances in computer games, pp.159-174, 2004.
, Scalability and Parallelization of Monte-Carlo Tree Search, Proceedings of Advance in Computer Games 13, 2010.
, Associating domain-dependent knowledge and Monte Carlo approaches within a Go program, Information Sciences, vol.175, issue.4, 2005.
, A Survey of Monte Carlo Tree Search Methods, Computational Intelligence and AI in Games, pp.1-43, 2012.
, Computational Intelligence and AI in Games, IEEE Transactions on, vol.41, pp.68-72, 2012.
, « Transpositions and move groups in Monte Carlo tree search, Computational Intelligence and Games CIG'08. IEEE Symposium On. IEEE, pp.389-395, 2008.
, « Combining tactical search and Monte-Carlo in the game of Go, IEEE CIG, pp.171-175, 2005.
, Computing Elo Ratings of Move Patterns in the Game of Go, Computer Games Workshop, pp.39-48, 2007.
, « Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search, Computers and Games, pp.72-83, 2007.
, Criticality : a Monte-Carlo Heuristic for Go Programs Invited talk at the University of Electro-Communications, p.68, 2009.
, Algorithmic Combinatorial Game Theory, Theoretical Computer Science, vol.3133, pp.325-338, 2004.
, « New results about impartial solitaire clobber, Operations Research, vol.43, pp.463-482, 2009.
, « Designing multi-objective multi-armed bandits algorithms : A study, Neural Networks (IJCNN) The 2013 International Joint Conference on. IEEE. 2013, pp.1-8
, « The Last-Good-Reply Policy for Monte-Carlo Go, International Computer Games Association Journal 32, pp.221-227, 2009.
, Fuego—An Open-Source Framework for Board Games and Go Engine Based on Monte Carlo Tree Search, IEEE Transactions on Computational Intelligence and AI in Games 2, pp.259-270, 2009.
DOI : 10.1109/TCIAIG.2010.2083662
, « Evaluation in Go by a Neural Network using Soft Segmentation, Advances in Computer Games : Many Games, Many Challenges, pp.97-108, 2003.
, « Learning Simulation Control in General Game-Playing Agents, pp.954-959
, Modification of UCT with Patterns in Monte-Carlo Go, Rapp. tech. INRIA, vol.46, pp.38-72, 2006.
, The Grand Challenge of Computer Go : Monte Carlo Tree Search and Extensions, Communications of the ACM, vol.553, issue.12, pp.106-113, 2012.
, Une contribution à l'apprentissage par renforcement, 2007.
, « Common fate graph patterns in Monte Carlo Tree Search for computer go, Computational Intelligence and Games (CIG), 2014 IEEE Conference on. Août 2014, pp.1-8
, « Learning on Graphs in the Game of Go, In : Artificial Neural Networks?ICANN, pp.347-352, 2001.
, Combining online and offline knowledge in UCT, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.273-280, 2007.
DOI : 10.1145/1273496.1273531
URL : https://hal.archives-ouvertes.fr/inria-00164003
, Achieving master level play in 9× 9 computer go, Proceedings of AAAI, pp.1537-1540, 2008.
, Monte-Carlo tree search and rapid action value estimation in computer Go, Artificial Intelligence, vol.175, issue.11, p.68, 2011.
DOI : 10.1016/j.artint.2011.03.007
, « On Semeai Detection in Monte-Carlo Go, Computers and Games, pp.14-25, 2014.
, « The Perceptual Cues that Reshape Expert Reasoning, Scientific Reports, vol.2, issue.6, 2012.
, Investigating the Limits of Monte-Carlo Tree Search Methods in Computer Go, The 8th international conference on Computers and Games (CG2013). 2013, p.83
DOI : 10.1007/978-3-319-09165-5_4
, All-Moves-As-First Heuristics in Monte- Carlo Go, Proceedings International Conference Artificial Intelligence, pp.605-610, 2009.
, « On a New Class of Codes for Identifying Vertices in Graphs, IEEE Transactions on Information Theory, vol.442, pp.599-611, 1998.
, Bandit Based Monte-Carlo Planning, Machine Learning : ECML 2006, pp.282-293, 2006.
DOI : 10.1007/11871842_29
, « Représentations émergentes : Une approche multi-agents des systèmes complexes adaptatifs en psychologie cognitive, Thèse de doct. Université Lumière Lyon, p.6, 2008.
, « Gradient-based learning applied to document recognition, Proceedings of the IEEE, pp.2278-2324, 1998.
, « Modeling Go Game as a Large Decomposable Decision Process
, « Markov Games as a Framework for Multi-Agent Reinforcement Learning, Proceedings of the Eleventh International Conference on Machine Learning, pp.157-163, 1994.
, Computers and games, pp.13-24, 2008.
, In : La Nature Revue des sciences et de leurs applications aux arts et à l'industrie. Journal hebdomadaire illustré Suivi de : Bulletin météorologique de La Nature, Boîte aux lettres, Nouvelles scientifiques : Quinzième année, deuxième semestre : n. 731 à 756, pp.1887-402
, « Monte-Carlo Tree Search for General Game Playing, 2008.
, Prototype at the Human machine competition in Barcelona 2010 : a Tournament Report and Analysis, Rapp. tech. Deptartement of Computing Science Canada, vol.36, pp.29-83, 2010.
, « Localizing Search in Monte-Carlo Go Using Statistical Covariance, ICGA Journal, vol.323, issue.69, pp.154-160, 2009.
, Artificial intelligence : a modern approach, 2010.
, « Nested rollout policy adaptation for Monte Carlo tree search, Proceedings of the Twenty-Second international joint conference on Artificial Intelligence-Volume Volume One, pp.649-654, 2011.
, Des récréations arithmétiques au corps des nombres surréels et à la victoire d'un programme aux échecs : une histoire de la théorie des jeux combinatoires au XXème siècle, Thèse de doct, p.2014
, « Trade-Offs in Sampling-Based Adversarial Planning, pp.202-209
, Multiple Overlapping Tiles for Contextual Monte Carlo Tree Search, Applications of Evolutionary Computation, vol.79, pp.201-210, 2010.
DOI : 10.1007/978-3-642-12239-2_21
URL : https://hal.archives-ouvertes.fr/inria-00456422
, Biasing Monte-Carlo Simulations through RAVE Values, Computers and Games, pp.59-68, 2011.
DOI : 10.1007/978-3-642-17928-0_6
URL : https://hal.archives-ouvertes.fr/inria-00485555
, « Grouping nodes for Monte-Carlo tree search, Computer Games Workshop, pp.276-283, 2007.
, Reinforcement Learning : An Introduction, pp.11-51, 1998.
, « Single-Player Monte-Carlo Tree Search, English. In : Computers and Games. Sous la dir. de H.Jaap van den HERIK, Xinhe XU, Zongmin MA et MarkH.M. WINANDS. T. 5131. Lecture Notes in Computer Science, pp.1-12, 2008.
, The History Heuristic and Alpha-Beta Search Enhancements in Practice ». In : Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.1111, pp.1203-1212, 1989.
, Temporal Difference Learning of Position Evaluation in the Game of Go, Advances in Neural Information Processing Systems, pp.817-817, 1994.
, On the scalability of parallel UCT, Computers and Games, pp.36-47, 2011.
, Bayesian pattern ranking for move prediction in the game of Go, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.873-880, 2006.
DOI : 10.1145/1143844.1143954
, « New Heuristics for Monte Carlo Tree Search Applied to the Game of Go, Thèse de doct, pp.1-40, 2011.
, Reinforcement learning and simulation-based search in computer Go, Thèse de doct, pp.1-68, 2009.
, Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning, Artificial Intelligence, vol.112, issue.1-2, pp.1-2, 1999.
DOI : 10.1016/S0004-3702(99)00052-1
, « Reinforcement Learning of Local Shape in the Game of Go, Proceedings of the 20th International Joint Conference on Artifical Intelligence. IJCAI'07, pp.1053-1058, 2007.
, Temporal-difference search in computer Go, Machine Learning, vol.3, issue.1, pp.1-37, 2012.
DOI : 10.1007/s10994-012-5280-0
, Monte-Carlo simulation balancing, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.945-952, 2009.
DOI : 10.1145/1553374.1553495
, « Computational Experiments with the RAVE Heuristic, Computers and Games, pp.69-80, 2011.
, N-Grams and the Last-Good-Reply Policy Applied in General Game Playing, Computational Intelligence and AI in Games, pp.73-83, 2012.
DOI : 10.1109/TCIAIG.2012.2200252
, « Revisiting Move Groups in Monte-Carlo Tree Search, Advances in Computer Games, pp.13-23, 2012.
, Modifications of UCT and sequence-like simulations for Monte-Carlo Go, 2007 IEEE Symposium on Computational Intelligence and Games, pp.175-182, 2007.
DOI : 10.1109/CIG.2007.368095
, « Multi-objective Monte-Carlo Tree Search, Asian conference on machine learning. T. 25. 2012, pp.507-522
, « A new selfacquired knowledge process for Monte Carlo Tree Search ». en, Computer Games Workshop, ECAI (European Conference on Artificial Intelligence). Août 2012, pp.13-24
, « Knowledge complement for Monte Carlo Tree Search : an application to combinatorial games ». en, IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp.997-1003, 2014.
, « A Self- Acquiring Knowledge Process for MCTS, International Journal on Artificial Intelligence Tools, pp.2015-87