论文标题
学习设计师的偏好以推动进化
Learning the Designer's Preferences to Drive Evolution
论文作者
论文摘要
本文介绍了设计器偏好模型，该模型是一种数据驱动的解决方案，该模型追求在质量多样性的混合发电性共同创造（QD MI-CC）工具中学习用户生成的数据，其目的是为用户的设计样式建模，以更好地评估该工具的程序生成的内容，并与该用户的偏好有关。通过这种方法，我们旨在将用户的代理在生成的内容上增加，以使用户工具相互刺激循环陷入困境，也不会用定期的建议挑剔用户疲劳。我们描述了这种新颖解决方案的细节及其在MI-CC工具中的实现，即进化地牢设计师。我们从进行的初始测试中介绍并讨论了我们的发现，从而发现了这一组合研究系列的开放挑战，该研究线将MI-CC与通过机器学习的程序内容产生相结合。
This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.
