  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.
