Game Generation via Large Language Models

Fuente: arXiv
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Autores principales: Hu, Chengpeng, Zhao, Yunlong, Liu, Jialin
Formato: Preprint
Publicado: 2024
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author Hu, Chengpeng
Zhao, Yunlong
Liu, Jialin
author_facet Hu, Chengpeng
Zhao, Yunlong
Liu, Jialin
contents Recently, the emergence of large language models (LLMs) has unlocked new opportunities for procedural content generation. However, recent attempts mainly focus on level generation for specific games with defined game rules such as Super Mario Bros. and Zelda. This paper investigates the game generation via LLMs. Based on video game description language, this paper proposes an LLM-based framework to generate game rules and levels simultaneously. Experiments demonstrate how the framework works with prompts considering different combinations of context. Our findings extend the current applications of LLMs and offer new insights for generating new games in the area of procedural content generation.
format Preprint
id arxiv_https___arxiv_org_abs_2404_08706
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Game Generation via Large Language Models
Hu, Chengpeng
Zhao, Yunlong
Liu, Jialin
Artificial Intelligence
Recently, the emergence of large language models (LLMs) has unlocked new opportunities for procedural content generation. However, recent attempts mainly focus on level generation for specific games with defined game rules such as Super Mario Bros. and Zelda. This paper investigates the game generation via LLMs. Based on video game description language, this paper proposes an LLM-based framework to generate game rules and levels simultaneously. Experiments demonstrate how the framework works with prompts considering different combinations of context. Our findings extend the current applications of LLMs and offer new insights for generating new games in the area of procedural content generation.
title Game Generation via Large Language Models
topic Artificial Intelligence
url https://arxiv.org/abs/2404.08706