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Toward Co-creative Dungeon Generation via Transfer Learning

2021-07-27 00:54:55
Zisen Zhou, Matthew Guzdial
arXiv_AI
           
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2107.12533

PDF

https://arxiv.org/pdf/2107.12533.pdf

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