Learning the Designer's Preferences to Drive Evolution
This work addresses user fatigue and agency in procedural content generation tools, but it appears incremental as it builds on existing mixed-initiative co-creativity and machine learning integration.
The paper tackles the problem of modeling user design preferences in a mixed-initiative co-creativity tool to improve content assessment, aiming to enhance user agency without causing fatigue. Initial tests were conducted, but no concrete numerical results are provided.
This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.