Towards MCTS for Creative Domains

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Cameron Browne: Towards MCTS for Creative Domains. In: Computational Creativity 2011 ICCC 2011, pp. 96-101.



Monte Carlo Tree Search (MCTS) has recently demon- strated considerable success for computer Go and other difficult AI problems. We present a general MCTS model that extends its application from searching for optimal actions in games and combinatorial optimisa- tion tasks to the search for optimal sequences and em- bedded subtrees. The primary application of this ex- tended MCTS model will be for creative domains, as it maps naturally to a range of procedural content genera- tion tasks for which Markovian or evolutionary ap- proaches would typically be used.

Extended Abstract


author = {Cameron Browne},
title = {Towards MCTS for Creative Domains},
editor = {Dan Ventura, Pablo Gervás, D. Fox Harrell, Mary Lou Maher, Alison Pease and Geraint Wiggins},
booktitle = {Proceedings of the Second International Conference on Computational Creativity},
series = {ICCC2011},
year = {2011},
month = {April},
location = {México City, México},
pages = {96-101},
url = {, },
publisher = {International Association for Computational Creativity},
keywords = {computational, creativity},

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