  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.
