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