Game Generation via Large Language Models

TL;DR

This paper presents a framework using large language models to generate both game rules and levels simultaneously, advancing procedural content generation for video games.

Contribution

It introduces a novel LLM-based approach for joint game rule and level generation from video game descriptions.

Findings

Framework effectively generates game rules and levels.

Prompt design impacts generation quality.

Extends LLM applications in game development.

Abstract

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.