  As academic interest in procedural content generation (PCG) for games has
increased, so has the need for methodologies for comparing and contrasting the
output spaces of alternative PCG systems. In this paper we introduce and
evaluate a novel approach for visualising the generative spaces of level
generation systems, using embeddings extracted from a trained convolutional
neural network. We evaluate the approach in terms of its ability to produce 2D
visualisations of encoded game levels that correlate with their behavioural
characteristics. The results across two alternative game domains, Super Mario
and Boxoban, indicate that this approach is powerful in certain settings and
that it has the potential to supersede alternative methods for visually
comparing generative spaces. However its performance was also inconsistent
across the domains investigated in this work, as well as it being susceptible
to intermittent failure. We conclude that this method is worthy of further
evaluation, but that future implementations of it would benefit from
significant refinement.
