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2023

Practical PCG Through Large Language Models

Nasir, Muhammad U, Togelius, Julian

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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.

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J. Togelius, G. N. Yannakakis, K. O. Stanley, and C. Browne, “Search-based procedural content generation: A taxonomy and survey,” IEEE Transactions on Computational Intelligence and AI in Games , vol. 3, no. 3, pp. 172–186, 2011
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N. Shaker, J. Togelius, and M. J. Nelson, “Procedural content generation in games,” 2016
2016
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A. Summerville, S. Snodgrass, M. Guzdial, C. Holmgård, A. K. Hoover, A. Isaksen, A. Nealen, and J. Togelius, “Procedural content generation via machine learning (pcgml),” IEEE Transactions on Games , vol. 10, no. 3, pp. 257–270, 2018
2018
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J. Xu and Z. Zhu, “Reinforced continual learning,” Advances in Neural Information Processing Systems , vol. 31, 2018
2018
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J. Harris, Exploring roguelike games . CRC Press, 2020
2020
Earlier work this paper cites.

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M. U. Nasir, S. Earle, J. Togelius, S. James, and C. Cleghorn, “Llmatic: Neural architecture search via large language models and quality-diversity optimization,” arXiv preprint arXiv:2306.01102 , 2023
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G. Todd, S. Earle, M. U. Nasir, M. C. Green, and J. Togelius, “Level generation through large language models,” in Proceedings of the 18th International Conference on the Foundations of Digital Games , 2023, pp. 1–8
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S. Sudhakaran, M. González-Duque, C. Glanois, M. Freiberger, E. Najarro, and S. Risi, “Mariogpt: Open-ended text2level generation through large language models,” arXiv preprint arXiv:2302.05981 , 2023
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