  The past decade has seen a rapid increase in the level of research interest
in procedural content generation (PCG) for digital games, and there are now
numerous research avenues focused on new approaches for driving and applying
PCG systems. An area in which progress has been comparatively slow is the
development of generalisable approaches for comparing alternative PCG systems,
especially in terms of their generative spaces. It is to this area that this
paper aims to make a contribution, by exploring the utility of data compression
algorithms in compressing the generative spaces of PCG systems. We hope that
this approach could be the basis for developing useful qualitative tools for
comparing PCG systems to help designers better understand and optimize their
generators. In this work we assess the efficacy of a selection of algorithms
across sets of levels for 2D tile-based games by investigating how much their
respective generative space compressions correlate with level behavioral
characteristics. We conclude that the approach looks to be a promising one
despite some inconsistency in efficacy in alternative domains, and that of the
algorithms tested Multiple Correspondence Analysis appears to perform the most
effectively.

Human-Computer Interaction