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Towards MCTS for Creative Domains

Cameron Browne

Conference or Workshop Paper
Second International Computational Creativity Conference (ICCC '11)
May, 2011

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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