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Towards MCTS for Creative Domains

Cameron Browne

Conference or Workshop Paper
Second International Computational Creativity Conference (ICCC '11)
LNCS
pp.96–101
May, 2011
Springer
Abstract

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.

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