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Techniques for procedural content generation via machine learning (PCGML)have been shown to be useful for generating novel game content. While usedprimarily for producing new content in the style of the game domain used fortraining, recent works have increasingly started to explore methods fordiscovering and generating content in novel domains via techniques such aslevel blending and domain transfer. In this paper, we build on these works andintroduce a new PCGML approach for producing novel game content spanningmultiple domains. We use a new affordance and path vocabulary to encode datafrom six different platformer games and train variational autoencoders on thisdata, enabling us to capture the latent level space spanning all the domainsand generate new content with varying proportions of the different domains.

Exploring Level Blending across Platformers via Paths and Affordances