Exploration Exploitation in Go: UCT for Monte-Carlo Go, Proceedings of NIPS'06, the 2006 Annual Conference on Neural Information Processing Systems, 2006. ,
URL : https://hal.archives-ouvertes.fr/hal-00115330
Hobby games market nearly $1.2 billion News article on hobby gaming's success, with notes on TCG market share, 2016. ,
Monte-Carlo Tree Reductions for Stochastic Games, Proceedings of TAAI'2014, the 2014 Conference on Technologies and Applications of Artificial Intelligence, pp.228-238, 2014. ,
DOI : 10.1007/978-3-319-13987-6_22
DeepStack: Expert-level artificial intelligence in heads-up no-limit poker, Science, vol.29, issue.6337, pp.508-513, 2017. ,
DOI : 10.1609/aimag.v31i4.2311
Construction of poker playing system considering strategies, Proceedings of CyberGames'06, the 2006 International Conference on Game Research and Development, pp.121-128, 2006. ,
Reinforcement learning of strategies for settlers of catan, 2004. ,
Artificial Intelligence: A Modern Approach, pp.161-201, 2009. ,
Best Reply Search for Multiplayer Games, IEEE Transactions on Computational Intelligence and AI in Games, vol.3, issue.1, pp.57-66, 2011. ,
DOI : 10.1109/TCIAIG.2011.2107323
Programming a Computer for Playing Chess, Philosophical Magazine, vol.41, pp.256-275, 1950. ,
DOI : 10.1007/978-1-4757-1968-0_1
Mastering the game of Go with deep neural networks and tree search, Nature, vol.34, issue.7587, pp.484-489, 2016. ,
DOI : 10.3233/ICG-2011-34302
Multi-Player Games: Algorithms and Approaches, 2003. ,
Monte-Carlo Tree Search in Settlers of Catan, Proceedings of ACG'09, the 2009 Conference on Advances in Computer Games, pp.21-32, 2009. ,
DOI : 10.1007/978-3-642-12993-3_3
URL : http://ticc.uvt.nl/~pspronck/pubs/ACG12Szita.pdf
Modification of UCT Algorithm with Quiescent Search in Computer GO, 2010 International Conference on Technologies and Applications of Artificial Intelligence, pp.481-484, 2010. ,
DOI : 10.1109/TAAI.2010.81