Procedural Content Generation via Generative Artificial Intelligence Article Swipe

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Xinyu Mao , Wanli Yu , Kazunori D Yamada , Michael R. Zielewski ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2407.09013

The attempt to utilize machine learning in PCG has been made in the past. In this survey paper, we investigate how generative artificial intelligence (AI), which saw a significant increase in interest in the mid-2010s, is being used for PCG. We review applications of generative AI for the creation of various types of content, including terrains, items, and even storylines. While generative AI is effective for PCG, one significant issues it faces is that building high-performance generative AI requires vast amounts of training data. Because content generally highly customized, domain-specific training data is scarce, and straightforward approaches to generative AI models may not work well. For PCG research to advance further, issues related to limited training data must be overcome. Thus, we also give special consideration to research that addresses the challenges posed by limited training data.

Related Topics

Generative Grammar

Artificial Intelligence

Computer Science

Mathematics

Mathematical Analysis

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Generative grammar Content (measure theory) Artificial intelligence Computer science Mathematics Mathematical analysis

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2407.09013
PDF: https://arxiv.org/pdf/2407.09013
OA Status: green
Cited By: 4
Related Works: 10
OpenAlex ID: https://openalex.org/W4400667193

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OpenAlex ID: https://openalex.org/W4400667193

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DOI: https://doi.org/10.48550/arxiv.2407.09013

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Title: Procedural Content Generation via Generative Artificial Intelligence

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-07-12

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Authors: Xinyu Mao, Wanli Yu, Kazunori D Yamada, Michael R. Zielewski

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Landing page: https://arxiv.org/abs/2407.09013

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PDF URL: https://arxiv.org/pdf/2407.09013

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Open access: Yes

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OA status: green

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OA URL: https://arxiv.org/pdf/2407.09013

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Concepts: Generative grammar, Content (measure theory), Artificial intelligence, Computer science, Mathematics, Mathematical analysis

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Cited by: 4

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Citations by year (recent): 2025: 3, 2024: 1

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Related works (count): 10

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