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The Exploring of AI Applications in Game Development
- 1 Faculty of Information Technology, Monash University, Melbourne, Victoria, Australian
* Author to whom correspondence should be addressed.
Abstract
Recent advances in artificial intelligence technologies have begun to transform the gaming industry, especially in the areas of player-character interaction and narrative development. Traditionally, game stories and character relationships are predefined through scripted dialogues and sequences, which requires developers to invest a lot of time and effort. However, AI-driven approaches such as large language models (LLMs) and deep learning techniques offer a dynamic alternative that can enable more flexible, player-driven interactions and adaptive AI behaviors. This paper comprehensively reviews the current role of AI in game design from multiple perspectives, including applications in multi-agent interaction, procedural level and game content generation, and game development process optimization. In addition, this study explores the advantages and limitations of AI technology, coping with technical challenges, and ethical issues that may arise during the implementation of AI. The results are intended to provide a reference for the future application of AI in game design and provide recommendations for coping with emerging risks.
Keywords
Artificial Intelligence, Game design, Language Models, Aiethics, AI-driven Game Development
[1]. Summerville, A., Snodgrass, S., Guzdial, M., Holmgård, C., Hoover, A. K., Isaksen, A., ... & Togelius, J. (2018). Procedural content generation via machine learning (PCGML). IEEE Transactions on Games, 10(3), 257–270.
[2]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
[3]. Jagdale, D. (2021). Finite state machine in game development. International Journal of Advanced Research in Science, Communication and Technology, 10(1).
[4]. Jiang, J., Dun, C., Huang, T., & Lu, Z. (2018). Graph convolutional reinforcement learning. arXiv preprint arXiv:1810.09202.
[5]. Sudhakaran, S., González-Duque, M., Freiberger, M., Glanois, C., Najarro, E., & Risi, S. (2023). Mariogpt: Open-ended text2level generation through large language models. Advances in Neural Information Processing Systems, 36, 54213–54227.
[6]. Cavazza, M., Pizzi, D., Charles, F., Vogt, T., & André, E. (2009). Emotional input for character-based interactive storytelling. Proceedings of the International Conference on Interactive Storytelling, 1–10.
[7]. Anjum, A., Li, Y., Law, N., Charity, M., & Togelius, J. (2024, May). The ink splotch effect: A case study on ChatGPT as a co-creative game designer. In Proceedings of the 19th International Conference on the Foundations of Digital Games (pp. 1–15).
[8]. Nasir, M. U., James, S., & Togelius, J. (2024). Word2World: Generating stories and worlds through large language models. arXiv preprint arXiv:2405.06686.
[9]. Todd, G., Earle, S., Nasir, M. U., Green, M. C., & Togelius, J. (2023). Level generation through large language models. In Proceedings of the 18th International Conference on the Foundations of Digital Games (pp. 1–8).
[10]. Kumaran, V., Rowe, J., Mott, B., & Lester, J. (2023, October). SceneCraft: Automating interactive narrative scene generation in digital games with large language models. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 19(1), 86–96.
[11]. Volz, V., Schrum, J., Liu, J., Lucas, S. M., Smith, A. M., & Risi, S. (2018, July). Evolving Mario levels in the latent space of a deep convolutional generative adversarial network. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 221–228). ACM.
[12]. Li,D.,Rawat,A.S.,Zaheer,M.,Wang,X.,Lukasik,M.,Veit,A.,Yu,F.,&Kumar,S.(2022).Largelanguagemodels with controllable working memory. arXiv preprint arXiv:2211.05110.
[13]. Gallotta, R., Todd, G., Zammit, M., Earle, S., Liapis, A., Togelius, J., & Yannakakis, G. N. (2024). Large language models and games: A survey and roadmap. IEEE Transactions on Games.
[14]. Riedl, M. O., & Zook, A. (2013, August). AI for game production. In 2013 IEEE Conference on Computational Intelligence in Games (CIG) (pp. 1–8). IEEE.
[15]. Filipović, A. (2023). The role of artificial intelligence in video game development. Kultura Polisa, 20(3),
[16]. Jagli, D. R., Chandra, D. S., Dhanikonda, S. R., & Laxmi, N. (2024). Artificial intelligence usage in game development. Preprints. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4929567
[17]. Yang, D., Kleinman, E., & Harteveld, C. (2024, August). GPT for games: A scoping review (2020–2023). In 2024 IEEE Conference on Games (CoG) (pp. 1–8). IEEE.
[18]. Deng, C., Duan, Y., Jin, X., Chang, H., Tian, Y., Liu, H., ... & Wang, H. (2024). Deconstructing the ethics of large language models from long-standing issues to new-emerging dilemmas. arXiv preprint arXiv:2406.05392.
[19]. Gamersky. (2024, October 19). Developers discuss performance issues in Dragon's Dogma 2: Related to NPC AI. Retrieved from https://www.gamersky.com/news/202410/1831886.shtml
Cite this article
Cui,Y. (2025). The Exploring of AI Applications in Game Development. Applied and Computational Engineering,158,59-64.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of CONF-SEML 2025 Symposium: Machine Learning Theory and Applications
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