Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning

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 question 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 algorithms such as Generative Adversarial Networks struggle in this domain and propose adaptations to improve their performance. In particular, we augment the neighbourhood of a Markov Random Fields approach to take into account not only local but also symmetric positional information. We conduct several empirical tests, including a user study that show the improvements achieved by the proposed modifications and obtain promising results.