  Co-creative Procedural Content Generation via Machine Learning (PCGML) refers
to systems where a PCGML agent and a human work together to produce output
content. One of the limitations of co-creative PCGML is that it requires
co-creative training data for a PCGML agent to learn to interact with humans.
However, acquiring this data is a difficult and time-consuming process. In this
work, we propose approximating human-AI interaction data and employing transfer
learning to adapt learned co-creative knowledge from one game to a different
game. We explore this approach for co-creative Zelda dungeon room generation.
