论文标题
通过机器学习捕获程序内容生成中的本地和全球模式
Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning
论文作者
论文摘要
通过机器学习（PCGML）方法生成的最新程序内容允许从现有内容中学习，以自动产生相似的内容。尽管这些方法能够为不同游戏（例如Super Mario Bros.，Doom，Zelda和Kid Icarus）生成内容，但这是一个悬而未决的问题，这些方法可以如何捕获大规模的视觉模式，例如对称性。在本文中，我们提出了匹配游戏作为一个域来测试PCGML算法的域，以生成合适的模式。我们证明，诸如生成对抗网络等流行算法在该领域挣扎，并提出改编以提高其性能。特别是，我们扩大了马尔可夫随机字段方法的附近，不仅要考虑本地，而且要考虑对称位置信息。我们进行了几项经验测试，包括一项用户研究，该研究表明提出的修改所取得的改进并获得有希望的结果。
Recent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically. While these approaches are able to generate content for different games (e.g. Super Mario Bros., DOOM, Zelda, and Kid Icarus), it is an open questions how well these approaches can capture large-scale visual patterns such as symmetry. In this paper, we propose match-three games as a domain to test PCGML algorithms regarding their ability to generate suitable patterns. We demonstrate that popular algorithm such as Generative Adversarial Networks struggle in this domain and propose adaptations to improve their performance. In particular we augment the neighborhood of a Markov Random Fields approach to not only take local but also symmetric positional information into account. We conduct several empirical tests including a user study that show the improvements achieved by the proposed modifications, and obtain promising results.
