Explainable PCGML via Game Design Patterns
  Procedural content generation via Machine Learning (PCGML) is the umbrella
term for approaches that generate content for games via machine learning. One
of the benefits of PCGML is that, unlike search or grammar-based PCG, it does
not require hand authoring of initial content or rules. Instead, PCGML relies
on existing content and black box models, which can be difficult to tune or
tweak without expert knowledge. This is especially problematic when a human
designer needs to understand how to manipulate their data or models to achieve
desired results. We present an approach to Explainable PCGML via Design
Patterns in which the design patterns act as a vocabulary and mode of
interaction between user and model. We demonstrate that our technique
outperforms non-explainable versions of our system in interactions with five
expert designers, four of whom lack any machine learning expertise.
