  Procedural Level Generation via Machine Learning (PLGML), the study of
generating game levels with machine learning, has received a large amount of
recent academic attention. For certain measures these approaches have shown
success at replicating the quality of existing game levels. However, it is
unclear the extent to which they might benefit human designers. In this paper
we present a framework for co-creative level design with a PLGML agent. In
support of this framework we present results from a user study and results from
a comparative study of PLGML approaches.
