Deep Learning for Procedural Content Generation

• URL: http://arxiv.org/abs/2010.04548v1
• Date: Fri, 9 Oct 2020 13:08:37 GMT
• Title: Deep Learning for Procedural Content Generation
• Authors: Jialin Liu, Sam Snodgrass, Ahmed Khalifa, Sebastian Risi, Georgios N. Yannakakis, Julian Togelius
• Abstract summary: A research field centered on content generation in games has existed for more than a decade. Deep learning has powered a remarkable range of inventions in content production. This article surveys the various deep learning methods that have been applied to generate game content directly or indirectly.
• Score: 14.533560910477693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Procedural content generation in video games has a long history. Existing procedural content generation methods, such as search-based, solver-based, rule-based and grammar-based methods have been applied to various content types such as levels, maps, character models, and textures. A research field centered on content generation in games has existed for more than a decade. More recently, deep learning has powered a remarkable range of inventions in content production, which are applicable to games. While some cutting-edge deep learning methods are applied on their own, others are applied in combination with more traditional methods, or in an interactive setting. This article surveys the various deep learning methods that have been applied to generate game content directly or indirectly, discusses deep learning methods that could be used for content generation purposes but are rarely used today, and envisages some limitations and potential future directions of deep learning for procedural content generation.

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