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Why Oatmeal is Cheap: Kolmogorov Complexity and Procedural Generation

arXiv 2305.02131 ↗First posted 3 May 2023cs.AI cs.HC cs.ITcited by 0

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Abstract (arXiv)

Although procedural generation is popular among game developers, academic research on the topic has primarily focused on new applications, with some research into empirical analysis. In this paper we relate theoretical work in information theory to the generation of content for games. We prove that there is a relationship between the Kolomogorov complexity of the most complex artifact a generator can produce, and the size of that generator's possibility space. In doing so, we identify the limiting relationship between the knowledge encoded in a generator, the density of its output space, and the intricacy of the artifacts it produces. We relate our result to the experience of expert procedural generator designers, and illustrate it with some examples.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 1 of its 10 references that are in the map. First posted on arXiv 3 May 2023.

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References · 1

  1. 2017Procedural Content Generation via Machine Learning (PCGML)cited by 137

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