Sampling Hyrule: Multi-Technique Probabilistic Level Generation for Action Role Playing Games
DOI:
https://doi.org/10.1609/aiide.v11i3.12817Keywords:
Artificial Intelligence, Procedural Content Generation, Bayesian Networks, Principal Component AnalysisAbstract
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.