Toward Co-creative Dungeon Generation via Transfer Learning
This addresses a bottleneck in co-creative PCGML for game developers, but it is incremental as it builds on existing transfer learning methods.
The paper tackles the problem of acquiring co-creative training data for human-AI interaction in procedural content generation by proposing to approximate this data and use transfer learning to adapt knowledge across games, specifically demonstrating it for Zelda dungeon room generation.
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.