论文标题
通过路径和负担探索平台跨平台的水平混合
Exploring Level Blending across Platformers via Paths and Affordances
论文作者
论文摘要
通过机器学习（PCGML）生成程序内容的技术已被证明可用于生成新型游戏内容。虽然主要用于以用于培训的游戏域的样式生产新内容，但最近的作品越来越多地开始探索通过水平混合和域传输等技术在新颖域中发现和生成内容的方法。在本文中，我们以这些作品为基础，并引入了一种新的PCGML方法，用于生产跨越多个领域的新型游戏内容。我们使用新的负担能力和路径词汇来编码来自六个不同平台游戏的数据，并在此数据上训练变异自动编码器，从而使我们能够捕获跨越所有域的潜在级别空间，并以不同比例的不同域生成新内容。
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.
