Monte Carlo tree search based algorithms for dynamic difficulty adjustment
暂无分享,去创建一个
Xiaodong Li | Fabio Zambetta | Florian Mueller | William L. Raffe | Marco Tamassia | Simon Demediuk | Xiaodong Li | F. Mueller | Fabio Zambetta | W. Raffe | Simon Demediuk | M. Tamassia
[1] Peta Wyeth,et al. The effect of multiplayer dynamic difficulty adjustment on the player experience of video games , 2014, CHI Extended Abstracts.
[2] Paul A. Cairns,et al. A grounded investigation of game immersion , 2004, CHI EA '04.
[3] Csaba Szepesvári,et al. Bandit Based Monte-Carlo Planning , 2006, ECML.
[4] Georgios N. Yannakakis,et al. Real-Time Game Adaptation for Optimizing Player Satisfaction , 2009, IEEE Transactions on Computational Intelligence and AI in Games.
[5] Simon M. Lucas,et al. A Survey of Monte Carlo Tree Search Methods , 2012, IEEE Transactions on Computational Intelligence and AI in Games.
[6] Jenova Chen,et al. Flow in games (and everything else) , 2007, CACM.
[7] Sonia Swart,et al. Mortal combat. , 2012, The Health service journal.
[8] Robin Hunicke,et al. AI for Dynamic Difficulty Adjustment in Games , 2004 .
[9] Tom Minka,et al. TrueSkillTM: A Bayesian Skill Rating System , 2006, NIPS.
[10] Beverley Callard. Street fighter. , 1998, Nursing standard (Royal College of Nursing (Great Britain) : 1987).
[11] Peta Wyeth,et al. GameFlow: a model for evaluating player enjoyment in games , 2005, CIE.
[12] Shawn A. Weil,et al. From Gaming to Training: A Review of Studies on Fidelity, Immersion, Presence, and Buy-in and Their Effects on Transfer in PC-Based Simulations and Games , 2005 .
[13] Julian Togelius,et al. Experience-Driven Procedural Content Generation , 2011, IEEE Trans. Affect. Comput..
[14] Paul A. Cairns,et al. Measuring and defining the experience of immersion in games , 2008, Int. J. Hum. Comput. Stud..
[15] Xiao Liu,et al. Dynamic Difficulty Adjustment of Game AI by MCTS for the game Pac-Man , 2010, 2010 Sixth International Conference on Natural Computation.
[16] Georgios N. Yannakakis,et al. Towards Capturing and Enhancing Entertainment in Computer Games , 2006, SETN.
[17] Xiaodong Li,et al. An Adaptive Training Framework for Increasing Player Proficiency in Games and Simulations , 2016, CHI PLAY.
[18] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[19] Darryl Charles,et al. Toward an understanding of flow in video games , 2008, CIE.
[20] J. Brophy. Motivating Students To Learn , 1987 .
[21] Thomas Hofmann,et al. TrueSkill™: A Bayesian Skill Rating System , 2007 .
[22] Vincent Corruble,et al. Extending Reinforcement Learning to Provide Dynamic Game Balancing , 2005 .
[23] Zbigniew Michalewicz,et al. Adapting to Human Gamers Using Coevolution , 2010, Advances in Machine Learning II.
[24] Thomas W. Malone,et al. What makes things fun to learn? heuristics for designing instructional computer games , 1980, SIGSMALL '80.
[25] Peter Auer,et al. Finite-time Analysis of the Multiarmed Bandit Problem , 2002, Machine Learning.
[26] Vincent Corruble,et al. Challenge-sensitive action selection: an application to game balancing , 2005, IEEE/WIC/ACM International Conference on Intelligent Agent Technology.
[27] Nicole Fruehauf. Flow The Psychology Of Optimal Experience , 2016 .
[28] David Silver,et al. Monte-Carlo tree search and rapid action value estimation in computer Go , 2011, Artif. Intell..