MCMCTS PCG 4 SMB: Monte Carlo Tree Search to Guide Platformer Level Generation
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[1] Guy Van den Broeck,et al. Monte-Carlo Tree Search in Poker Using Expected Reward Distributions , 2009, ACML.
[2] Cameron Browne. UCT for PCG , 2013, 2013 IEEE Conference on Computational Inteligence in Games (CIG).
[3] Csaba Szepesvári,et al. Bandit Based Monte-Carlo Planning , 2006, ECML.
[4] Julian Togelius,et al. The 2010 Mario AI Championship: Level Generation Track , 2011, IEEE Transactions on Computational Intelligence and AI in Games.
[5] Graeme Ritchie,et al. Some Empirical Criteria for Attributing Creativity to a Computer Program , 2007, Minds and Machines.
[6] Simon M. Lucas,et al. A Survey of Monte Carlo Tree Search Methods , 2012, IEEE Transactions on Computational Intelligence and AI in Games.
[7] Michèle Sebag,et al. The grand challenge of computer Go , 2012, Commun. ACM.
[8] Santiago Ontañón,et al. A Hierarchical Approach to Generating Maps Using Markov Chains , 2014, AIIDE.
[9] Peter I. Cowling,et al. Monte Carlo search applied to card selection in Magic: The Gathering , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.
[10] Matthew Guzdial,et al. Toward Game Level Generation from Gameplay Videos , 2016, ArXiv.
[11] Julian Togelius,et al. Modeling player experience in Super Mario Bros , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.
[12] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[13] Julian Togelius,et al. Linear levels through n-grams , 2014, MindTrek.
[14] Santiago Ontañón,et al. Generating Maps Using Markov Chains , 2013, AIIDE 2013.
[15] Stephen J. Guy,et al. Generating Believable Stories in Large Domains , 2013, Intelligent Narrative Technologies.