AAAI Publications, Eleventh Artificial Intelligence and Interactive Digital Entertainment Conference

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Sampling Hyrule: Multi-Technique Probabilistic Level Generation for Action Role Playing Games
Adam James Summerville, Michael Mateas

Last modified: 2015-09-23


Procedural Content Generation (PCG) using machine learning is a fast growing area of research. Action Role Playing Game (ARPG) levels represent an interesting challenge for PCG due to their multi-tiered structure and nonlinearity. Previous work has used Bayes Nets (BN) to learn properties of the topological structure of levels from The Legend of Zelda. In this paper we describe a method for sampling these learned distributions to generate valid, playable level topologies. We carry this deeper and learn a sampleable representation of the individual rooms using Principal Component Analysis. We combine the two techniques and present a multi-scale machine learned technique for procedurally generating ARPG levels from a corpus of levels from The Legend of Zelda.


Artificial Intelligence; Procedural Content Generation; Bayesian Networks; Principal Component Analysis

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