  Autonomous game design, generating games algorithmically, has been a longtime
goal within the technical games research field. However, existing autonomous
game design systems have relied in large part on human-authoring for game
design knowledge, such as fitness functions in search-based methods. In this
paper, we describe an experiment to attempt to learn a human-like fitness
function for autonomous game design in an adversarial manner. While our
experimental work did not meet our expectations, we present an analysis of our
system and results that we hope will be informative to future autonomous game
design research.
