Boardwalk: Towards a Framework for Creating Board Games with LLMs
Resumo
Introduction: Implementing board games in code can be a timeconsuming task. However, Large Language Models (LLMs) have been proven effective at generating code for domain-specific tasks with simple contextual information. Objective: We aim to investigate whether LLMs can implement digital versions of board games from rules described in natural language. This would be a step towards an LLM-assisted framework for quick board game code generation. We expect to determine the main challenges for LLMs to implement the board games, and how different approaches and models compare to one another. Methodology: We task three state-of-the-art LLMs (Claude, DeepSeek and ChatGPT) with coding a selection of 12 popular and obscure games in free-form and within Boardwalk, our proposed General Game Playing API. We anonymize the games and components to avoid evoking pre-trained LLM knowledge. The implementations are tested for playability and rule compliance. We evaluate success rate and common errors across LLMs and game popularity. Results: Our approach proves viable, with the best performing model, Claude 3.7 Sonnet, yielding 55.6% of games without any errors. While compliance with the API increases error frequency, the severity of errors is more significantly dependent on the LLM. We outline future steps for creating a framework to integrate this process, making the elaboration of board games more accessible.
Palavras-chave:
Board games, Large Language Models, Procedural Content Generation
Referências
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Piette, E., Soemers, D., Stephenson, M., Sironi, C., Winands, M., and Browne, C. (2020). Ludii – the ludemic general game system. Frontiers in Artificial Intelligence and Applications, 325:411–418.
Sudhakaran, S., González-Duque, M., Freiberger, M., Glanois, C., Najarro, E., and Risi, S. (2023). MarioGPT: open-ended text2level generation through large language models. In Proceedings of the 37th International Conference on Neural Information Processing Systems, NIPS ’23, Red Hook, NY, USA. Curran Associates Inc.
Sun, Y., Li, Z., Fang, K., Lee, C. H., and Asadipour, A. (2023). Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights Using Generative AI. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 19(1):425–434.
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Todd, G., Padula, A. G., Stephenson, M., Piette, É., Soemers, D., and Togelius, J. (2024). GAVEL: Generating games via evolution and language models. Advances in Neural Information Processing Systems, 37:110723–110745.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D. (2020). Language Models are Few-Shot Learners. In Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., and Lin, H., editors, Advances in Neural Information Processing Systems, volume 33, pages 1877–1901. Curran Associates, Inc.
Browne, C. (2011). Evolutionary Game Design. SpringerBriefs in Computer Science. Springer London, London, UK.
Coignion, T., Quinton, C., and Rouvoy, R. (2024). A Performance Study of LLM-Generated Code on Leetcode. In Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering, pages 79–89.
Genesereth, M., Love, N., and Pell, B. (2005). General game playing: Overview of the AAAI competition. AI magazine, 26(2):62–62.
Hu, C., Zhao, Y., and Liu, J. (2024). Game Generation via Large Language Models. In 2024 IEEE Conference on Games (CoG), pages 1–4.
Jiang, J., Wang, F., Shen, J., Kim, S., and Kim, S. (2024). A Survey on Large Language Models for Code Generation. arXiv preprint arXiv:2406.00515.
Kumaran, V., Rowe, J., Mott, B., and Lester, J. (2023). SceneCraft: Automating Interactive Narrative Scene Generation in Digital Games with Large Language Models. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 19(1):86–96.
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Ludii Portal (2020). Ultimate Tic-Tac-Toe. [link], accessed in Apr 21, 2025.
OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., Avila, R., Babuschkin, I., Balaji, S., Balcom, V., Baltescu, P., Bao, H., Bavarian, M., Belgum, J., Bello, I., Berdine, J., Bernadett-Shapiro, G., Berner, C., Bogdonoff, L., Boiko, O., Boyd, M., Brakman, A.-L., Brockman, G., Brooks, T., Brundage, M., Button, K., Cai, T., Campbell, R., Cann, A., Carey, B., Carlson, C., Carmichael, R., Chan, B., Chang, C., Chantzis, F., Chen, D., Chen, S., Chen, R., Chen, J., Chen, M., Chess, B., Cho, C., Chu, C., Chung, H. W., Cummings, D., Currier, J., Dai, Y., Decareaux, C., Degry, T., Deutsch, N., Deville, D., Dhar, A., Dohan, D., Dowling, S., Dunning, S., Ecoffet, A., Eleti, A., Eloundou, T., Farhi, D., Fedus, L., Felix, N., Fishman, S. P., Forte, J., Fulford, I., Gao, L., Georges, E., Gibson, C., Goel, V., Gogineni, T., Goh, G., Gontijo-Lopes, R., Gordon, J., Grafstein, M., Gray, S., Greene, R., Gross, J., Gu, S. S., Guo, Y., Hallacy, C., Han, J., Harris, J., He, Y., Heaton, M., Heidecke, J., Hesse, C., Hickey, A., Hickey, W., Hoeschele, P., Houghton, B., Hsu, K., Hu, S., Hu, X., Huizinga, J., Jain, S., Jain, S., Jang, J., Jiang, A., Jiang, R., Jin, H., Jin, D., Jomoto, S., Jonn, B., Jun, H., Kaftan, T., Łukasz Kaiser, Kamali, A., Kanitscheider, I., Keskar, N. S., Khan, T., Kilpatrick, L., Kim, J. W., Kim, C., Kim, Y., Kirchner, J. H., Kiros, J., Knight, M., Kokotajlo, D., Łukasz Kondraciuk, Kondrich, A., Konstantinidis, A., Kosic, K., Krueger, G., Kuo, V., Lampe, M., Lan, I., Lee, T., Leike, J., Leung, J., Levy, D., Li, C. M., Lim, R., Lin, M., Lin, S., Litwin, M., Lopez, T., Lowe, R., Lue, P., Makanju, A., Malfacini, K., Manning, S., Markov, T., Markovski, Y., Martin, B., Mayer, K., Mayne, A., McGrew, B., McKinney, S. M., McLeavey, C., McMillan, P., McNeil, J., Medina, D., Mehta, A., Menick, J., Metz, L., Mishchenko, A., Mishkin, P., Monaco, V., Morikawa, E., Mossing, D., Mu, T., Murati, M., Murk, O., Mély, D., Nair, A., Nakano, R., Nayak, R., Neelakantan, A., Ngo, R., Noh, H., Ouyang, L., O’Keefe, C., Pachocki, J., Paino, A., Palermo, J., Pantuliano, A., Parascandolo, G., Parish, J., Parparita, E., Passos, A., Pavlov, M., Peng, A., Perelman, A., de Avila Belbute Peres, F., Petrov, M., de Oliveira Pinto, H. P., Michael, Pokorny, Pokrass, M., Pong, V. H., Powell, T., Power, A., Power, B., Proehl, E., Puri, R., Radford, A., Rae, J., Ramesh, A., Raymond, C., Real, F., Rimbach, K., Ross, C., Rotsted, B., Roussez, H., Ryder, N., Saltarelli, M., Sanders, T., Santurkar, S., Sastry, G., Schmidt, H., Schnurr, D., Schulman, J., Selsam, D., Sheppard, K., Sherbakov, T., Shieh, J., Shoker, S., Shyam, P., Sidor, S., Sigler, E., Simens, M., Sitkin, J., Slama, K., Sohl, I., Sokolowsky, B., Song, Y., Staudacher, N., Such, F. P., Summers, N., Sutskever, I., Tang, J., Tezak, N., Thompson, M. B., Tillet, P., Tootoonchian, A., Tseng, E., Tuggle, P., Turley, N., Tworek, J., Uribe, J. F. C., Vallone, A., Vijayvergiya, A., Voss, C., Wainwright, C., Wang, J. J., Wang, A., Wang, B., Ward, J., Wei, J., Weinmann, C., Welihinda, A., Welinder, P., Weng, J., Weng, L., Wiethoff, M., Willner, D., Winter, C., Wolrich, S., Wong, H., Workman, L., Wu, S., Wu, J., Wu, M., Xiao, K., Xu, T., Yoo, S., Yu, K., Yuan, Q., Zaremba, W., Zellers, R., Zhang, C., Zhang, M., Zhao, S., Zheng, T., Zhuang, J., Zhuk, W., and Zoph, B. (2024). GPT-4 Technical Report.
Piette, E., Soemers, D., Stephenson, M., Sironi, C., Winands, M., and Browne, C. (2020). Ludii – the ludemic general game system. Frontiers in Artificial Intelligence and Applications, 325:411–418.
Sudhakaran, S., González-Duque, M., Freiberger, M., Glanois, C., Najarro, E., and Risi, S. (2023). MarioGPT: open-ended text2level generation through large language models. In Proceedings of the 37th International Conference on Neural Information Processing Systems, NIPS ’23, Red Hook, NY, USA. Curran Associates Inc.
Sun, Y., Li, Z., Fang, K., Lee, C. H., and Asadipour, A. (2023). Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights Using Generative AI. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 19(1):425–434.
Tanaka, T. and Simo-Serra, E. (2024). Grammar-based Game Description Generation using Large Language Models. IEEE Transactions on Games, pages 1–14.
Todd, G., Earle, S., Nasir, M. U., Green, M. C., and Togelius, J. (2023). Level Generation Through Large Language Models. In Proceedings of the 18th International Conference on the Foundations of Digital Games, FDG ’23, New York, NY, USA. Association for Computing Machinery.
Todd, G., Padula, A. G., Stephenson, M., Piette, É., Soemers, D., and Togelius, J. (2024). GAVEL: Generating games via evolution and language models. Advances in Neural Information Processing Systems, 37:110723–110745.
Publicado
30/09/2025
Como Citar
BECKER, Álvaro Guglielmin; OLIVEIRA, Gabriel Bauer de; ROSSATO, Lana Bertoldo; TAVARES, Anderson Rocha.
Boardwalk: Towards a Framework for Creating Board Games with LLMs. In: SIMPÓSIO BRASILEIRO DE JOGOS E ENTRETENIMENTO DIGITAL (SBGAMES), 24. , 2025, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 655-667.
DOI: https://doi.org/10.5753/sbgames.2025.10222.
