Co-Creative Level Design via Machine Learning
This work addresses the gap in understanding how PLGML can assist human game designers, though it appears incremental as it builds on existing PLGML methods.
The paper tackles the problem of unclear benefits of procedural level generation via machine learning (PLGML) for human designers by presenting a co-creative level design framework, with results from a user study and comparative analysis of PLGML approaches.
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.