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

• URL: http://arxiv.org/abs/2005.12579v1
• Date: Tue, 26 May 2020 08:58:37 GMT
• Title: Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning
• Authors: Vanessa Volz and Niels Justesen and Sam Snodgrass and Sahar Asadi and Sami Purmonen and Christoffer Holmg\r{a}rd and Julian Togelius and Sebastian Risi
• Abstract summary: Recent procedural content generation via machine learning (PCGML) methods allow learning to produce similar content from existing content. It is an open questions how well these approaches can capture large-scale visual patterns such as symmetry. In this paper, we propose to match-three games as a domain to test PCGML algorithms regarding their ability to generate suitable patterns.
• Score: 9.697217570243845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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