UCT for PCG

This paper describes initial experiments in the use of UCT-based algorithms for procedural content generation in creative game-like domains. UCT search offers potential benefits for this task, as its systematic method of node expansion constitutes an inherent form of exhaustive local search. A new variant called upper confidence bounds for graphs (UCG) is described, suitable for bitstring domains with reversible operations, such as those to which genetic algorithms are typically applied. We compare the performance of UCT-based methods with known search methods for two test domains, with encouraging results.

[1]  A. E. Eiben,et al.  Maintaining Population Diversity in Evolutionary Art , 2012, EvoMUSART.

[2]  Frédéric Maire,et al.  Evolutionary Game Design , 2011, IEEE Transactions on Computational Intelligence and AI in Games.

[3]  Sandra L. Berger.  Massachusetts , 1896, The Journal of comparative medicine and veterinary archives.

[4]  Graeme Ritchie,et al.  Some Empirical Criteria for Attributing Creativity to a Computer Program , 2007, Minds and Machines.

[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]  Julian Togelius,et al.  Search-Based Procedural Content Generation: A Taxonomy and Survey , 2011, IEEE Transactions on Computational Intelligence and AI in Games.

[7]  Clive Richards,et al.  The Blind Watchmaker , 1987, Bristol Medico-Chirurgical Journal.

[8]  W. Marsden.  I and J , 2012 .

[9]  Tristan Cazenave,et al.  Monte-Carlo Expression Discovery , 2013, Int. J. Artif. Intell. Tools.

[10]  Vidroha Debroy,et al.  Genetic Programming , 1998, Lecture Notes in Computer Science.

[11]  Cameron Browne.  Deductive search for logic puzzles , 2013, 2013 IEEE Conference on Computational Inteligence in Games (CIG).

[12]  Markus Püschel,et al.  Bandit-based optimization on graphs with application to library performance tuning , 2009, ICML '09.

[13]  Kenneth A. De Jong,et al.  Measurement of Population Diversity , 2001, Artificial Evolution.

[14]  Thierry Moudenc,et al.  Introduction of a new paraphrase generation tool based on Monte-Carlo sampling , 2009, ACL.

[15]  Csaba Szepesvári,et al.  Bandit Based Monte-Carlo Planning , 2006, ECML.

[16]  Jean Méhat,et al.  UCD: Upper Confidence Bound for Rooted Directed Acyclic Graphs , 2010, 2010 International Conference on Technologies and Applications of Artificial Intelligence.

[17]  Cameron Browne,et al.  Connection Games - Variations on a Theme , 2005 .