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.

翻译：通过机器学习(PCGML)生成共同程序内容是指PCGML代理和人类共同努力生成产出内容的系统。 共同创造的PCGML的局限性之一是,它需要共同创造的培训数据,使PCGML代理学会与人类互动。然而,获取这些数据是一个困难和耗时的过程。在这项工作中,我们提议使用类似的人类-AI互动数据,并采用转移学习来将学到的共生知识从一个游戏转换到另一个游戏。我们探索这一方法来生成共同创造Zelda地牢。