AAAI Publications, Eleventh Artificial Intelligence and Interactive Digital Entertainment Conference

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MCMCTS PCG 4 SMB: Monte Carlo Tree Search to Guide Platformer Level Generation
Adam James Summerville, Shweta Philip, Michael Mateas

Last modified: 2015-09-23


Markov chains are an enticing option for machine learned generation of platformer levels, but offer poor control for designers and are likely to produce unplayable levels. In this paper we present a method for guiding Markov chain generation using Monte Carlo Tree Search that we call Markov Chain Monte Carlo Tree Search (MCMCTS). We demonstrate an example use for this technique by creating levels trained on a corpus of levels from Super Mario Bros. We then present a player modeling study that was run with the hopes of using the data to better inform the generation of levels in future work.


MCTS; Monte Carlo Tree Search; Markov Chain; PCG; Procedural Content Generation; Game Design

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