Practical PCG Through Large Language Models

Autor:	Nasir, Muhammad U, Togelius, Julian
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Large Language Models (LLMs) have proven to be useful tools in various domains outside of the field of their inception, which was natural language processing. In this study, we provide practical directions on how to use LLMs to generate 2D-game rooms for an under-development game, named Metavoidal. Our technique can harness the power of GPT-3 by Human-in-the-loop fine-tuning which allows our method to create 37% Playable-Novel levels from as scarce data as only 60 hand-designed rooms under a scenario of the non-trivial game, with respect to (Procedural Content Generation) PCG, that has a good amount of local and global constraints. Comment: Published at 2023 IEEE Conference on Games
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.18243 Zobrazit plný text záznamu View this record from Arxiv