  In mixed-initiative co-creation tasks, wherein a human and a machine jointly
create items, it is important to provide multiple relevant suggestions to the
designer. Quality-diversity algorithms are commonly used for this purpose, as
they can provide diverse suggestions that represent salient areas of the
solution space, showcasing designs with high fitness and wide variety. Because
generated suggestions drive the search process, it is important that they
provide inspiration, but also stay aligned with the designer's intentions.
Additionally, often many interactions with the system are required before the
designer is content with a solution. In this work, we tackle these challenges
with an interactive constrained MAP-Elites system that leverages emitters to
learn the preferences of the designer and then use them in automated steps. By
learning preferences, the generated designs remain aligned with the designer's
intent, and by applying automatic steps, we generate more solutions per user
interaction, giving a larger number of choices to the designer and thereby
speeding up the search. We propose a general framework for preference-learning
emitters (PLEs) and apply it to a procedural content generation task in the
video game Space Engineers. We built an interactive application for our
algorithm and performed a user study with players.
