In this article, we introduce the concept of procedural content generation via knowledge transformation (PCG-KT), a new lens and framework for characterizing PCG methods and approaches in which content generation is enabled by the process of knowledge transformation: transforming knowledge derived from one domain in order to apply it in another. Our work is motivated by a substantial number of recent PCG works that focus on generating novel content via repurposing derived knowledge. Such works have involved, for example, performing transfer learning on models trained on one game's content to adapt to another game's content, as well as recombining different generative distributions to blend the content of two or more games. Such approaches arose in part due to limitations in PCG via machine learning, such as producing generative models for games lacking training data and generating content for entirely new games. In this article, we categorize such approaches under this new lens of PCG-KT by offering a definition and framework for describing such methods and surveying existing works using this framework. Finally, we conclude by highlighting open problems and directions for future research in this area.

中文翻译：

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通过知识转换生成程序内容（PCG-KT）

在本文中，我们介绍了通过知识转换生成程序内容（PCG-KT）的概念，这是一种新的视角和框架，用于描述 PCG 方法和方法，其中内容生成是通过知识转换过程实现的：转换源自知识的知识域以便将其应用到另一个域中。我们的工作受到最近大量 PCG 作品的推动，这些作品专注于通过重新利用派生知识来生成新颖的内容。例如，此类工作涉及对一个游戏内容训练的模型进行迁移学习以适应另一游戏的内容，以及重新组合不同的生成分布以混合两个或多个游戏的内容。出现此类方法的部分原因是机器学习在 PCG 中的局限性，例如为缺乏训练数据的游戏生成生成模型以及为全新游戏生成内容。在本文中，我们通过提供描述此类方法的定义和框架并使用该框架调查现有工作，在 PCG-KT 的新视角下对此类方法进行分类。最后，我们强调了该领域未来研究的开放问题和方向。