Worlds Without Handcrafted Limits: A Narrative Review of Procedural Content Generation from Perlin Noise to Machine Learning

Abstract

Procedural content generation (PCG)---the algorithmic creation of game levels, terrain, quests, and rules---has evolved from a memory-saving trick into one of game development's most active research areas. This article presents a narrative review of the field's canonical line: Perlin's 1985 image synthesizer, the search-based taxonomy of Togelius and colleagues, the ACM survey of Hendrikx and colleagues, the Springer volume of Shaker, Togelius, and Nelson, the AI-and-games synthesis of Yannakakis and Togelius, the machine-learning turn of Summerville and colleagues, and the reinforcement-learning frontier of Khalifa and colleagues. The synthesis is organized around three themes: foundations, in which noise functions, grammars, and search established the generative toolbox; design, in which PCG met authorship---level design as search space, evolution as game designer, mixed-initiative tools; and learning, in which generative models trained on human-authored corpora opened PCGML and its controllability problem. It is concluded that PCG's history is the progressive relocation of authorship---from the asset to the generator---and that controllability is the field's central open problem.