Techniques for procedural content generation via machine learning (PCGML)
have been shown to be useful for generating novel game content. While used
primarily for producing new content in the style of the game domain used for
training, recent works have increasingly started to explore methods for
discovering and generating content in novel domains via techniques such as
level blending and domain transfer. In this paper, we build on these works and
introduce a new PCGML approach for producing novel game content spanning
multiple domains. We use a new affordance and path vocabulary to encode data
from six different platformer games and train variational autoencoders on this
data, enabling us to capture the latent level space spanning all the domains
and generate new content with varying proportions of the different domains.

本研究探讨了通过机器学习进行程序化内容生成技术在游戏领域的应用，着重介绍了如何基于多个游戏领域构建新的自动生成游戏关卡的方法，使用了多个游戏平台的数据，并训练了变分自编码器以跨越各个领域的潜在关卡空间来生成新的内容。