МЕТОДИ ПРОЦЕДУРНОЇ ТА ГЕНЕРАТИВНОЇ ПОБУДОВИ ІГРОВОГО КОНТЕНТУ

H. A. Zavhorodnia; Ya. I. Kornaga; V. V. Zavhorodnii

H. A. Zavhorodnia National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” https://orcid.org/0000-0001-8523-1761
Ya. I. Kornaga National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” https://orcid.org/0000-0001-9768-2615
V. V. Zavhorodnii National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” https://orcid.org/0000-0002-8347-7183

Keywords: procedural generation, game content, rule-based systems, stochastic methods, hybrid models, algorithms, computer games, automatic generation

Abstract

This paper investigates contemporary methods of procedural and generative game content creation, with a focus on rulebased approaches and stochastic models, as well as the challenges of integrating these methods to improve generation efficiency. Special attention is given to the problems of scalable content generation under limited computational resources and high requirements for variability, structural consistency, and realism of game environments. The study analyzes current trends in automatic content generation, including algorithmic rules, probabilistic models, Markov processes, stochastic grammars, and hybrid approaches that combine the advantages of deterministic and random mechanisms. The work proposes a formalization of the content generation process as a composition of deterministic and stochastic operators, allowing for simultaneous improvement of controllability and diversity of results. A mathematical model of the generative process is introduced, based on a state distribution function and a constraint function, ensuring the validity of generated game configurations. The study demonstrates how combining rule-based systems with stochastic generators enables a balance between predictability and variability of results, providing flexible adaptation to user requirements and dynamic game processes. Particular attention is paid to analyzing the efficiency of different approaches in terms of computational complexity, scalability, and quality of generated content. A series of experimental studies were conducted comparing the proposed hybrid model with baseline generation algorithms, including pure rule-based systems and random stochastic generators. The results show that hybrid methods can significantly increase content diversity (by 35–50 %) while maintaining structural integrity, and also reduce generation time compared to classical approaches. The study confirms that integrating deterministic and stochastic mechanisms is an effective approach to improving the quality of procedural content in modern game systems. The obtained results can be applied in the development of procedural worlds, adaptive generative environments, and modern computer games, ensuring a balance between predictability, variability, and computational efficiency.

References

1. Togelius J., Shaker N., Nelson M. Procedural Content Generation in Games. Springer, 2016. URL: https://link.springer.com/book/10.1007/978-3-319-42716-4
2. Hendrikx M., Meijer S., Van Der Velden J., Iosup A. Procedural content generation for games: A survey. ACM TOMM. 2013. URL: https://dl.acm.org/doi/10.1145/2422956.2422957
3. Завгородня Г. А., Завгородній В. В. Оптимізація продуктивності комп’ютерних ігор на основі методів машинного навчання. Системи та технології. 2026. Т. 71, № 1. С. 45–51. DOI: https://doi.org/10.32782/2521-6643-2026-1-71.6
4. Summerville A. et al. Procedural content generation via machine learning: A survey. IEEE Transactions on Games. 2018. DOI: https://doi.org/10.1109/TG.2018.2846639
5. Khalifa A., Togelius J. PCG via reinforcement learning. arXiv, 2020. DOI: https://doi.org/10.48550/arXiv.2001.09212
6. Goodfellow I., Bengio Y., Courville A. Deep Learning. MIT Press, 2016. URL: https://www.deeplearningbook.org/
7. Завгородня Г. А., Завгородній В. В. Методологія розроблення системи адаптивної генерації контенту. Технології та інжиніринг. 2025. Т. 26, № 5. С. 21–30. DOI: https://doi.org/10.30857/2786-5371.2025.5.2
8. Yannakakis G. N., Togelius J. Artificial Intelligence and Games. Springer, 2018. URL: https://link.springer.com/book/10.1007/978-3-319-63519-4
9. Завгородній В. В. та ін. Метод автоматичної генерації контенту на основі процедурних алгоритмів. Вчені записки ТНУ. 2022. Т. 33 (72), № 1. С. 91–96. DOI: https://doi.org/10.32838/2663-5941/2022.1/15
10. Radford A. et al. Language models are few-shot learners. arXiv, 2020. DOI: https://doi.org/10.48550/arXiv.2005.14165
11. Завгородня Г. А., Завгородній В. В. Розробка масштабованої розподіленої архітектури для MMO-систем. Вісник ХНТУ. 2025. № 4(95). С. 99–106. DOI: https://doi.org/10.35546/kntu2078-4481.2025.4.3.11
12. Browne C. et al. A survey of Monte Carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in Games. 2012. DOI: https://doi.org/10.1109/TCIAIG.2012.2186810
13. Volz V., Schrum J., Liu J., Lucas S. M., Smith A. D., & Risi S. Evolving Mario levels in the latent space of a deep convolutional generative adversarial network. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18). 2018. pp. 221–228. URL: https://arxiv.org/abs/1805.00728
14. Silver D. et al. Mastering the game of Go with deep neural networks and tree search. Nature. 2016. Vol. 529. С. 484–489. DOI: https://doi.org/10.1038/nature16961
15. Goodfellow I. Generative adversarial networks. arXiv, 2014. DOI: https://doi.org/10.48550/arXiv.1406.2661
16. Liapis A., Yannakakis G., Togelius J. Sentient Sketchbook: Computer-aided game level authoring. IEEE Transactions on Games. 2013. DOI: https://doi.org/10.1109/TCIAIG.2013.2297681
17. Snodgrass S., Ontañón S. Learning to generate video game maps using Markov models. IEEE Transactions on Computational Intelligence and AI in Games. 2017. DOI: https://doi.org/10.1109/TCIAIG.2016.2623560
18. Smith G., Whitehead J., Mateas M. Tanagra: Reactive planning and constraint solving for mixed initiative level design. IEEE Transactions on Computational Intelligence and AI in Games. 2011. DOI: https://doi.org/10.1109/TCIAIG.2011.2159716
19. Dutra P. V. M., Villela S. M., Fonseca Neto R. A mixed-initiative design framework for procedural content generation using reinforcement learning. Entertainment Computing. 2025. DOI: https://doi.org/10.1016/j.entcom.2024.100759
20. Risi S., & Preuss M. From Chess and Atari to StarCraft and beyond: how game AI is driving the world of AI. Künstliche Intelligenz. 2020. Vol. 34(1). С. 7–17. DOI: https://doi.org/10.1007/s13218-020-00647-w
21. Qi Z. Research on the application of generative artificial intelligence in games. ACE – Advances in Computer Engineering and Education, 2025. DOI: https://doi.org/10.54254/2755-2721/2025.18827
22. Vaswani A. et al. Attention is all you need. arXiv, 2017. DOI: https://doi.org/10.48550/arXiv.1706.03762
23. Dormans J. Adventures in level design: generating missions and spaces for action adventure games. Proceedings of the 2010 Workshop on Procedural Content Generation in Games (PCG’10). 2010. DOI: https://doi.org/10.1145/1814256.1814257
24. Pathak D. et al. Curiosity-driven exploration by self-supervised prediction. arXiv, 2017. DOI: https://doi.org/10.48550/arXiv.1705.05363
25. Sutton R., Barto A. Reinforcement learning: an introduction. MIT Press, 2018. URL: http://incompleteideas.net/book/the-book.html

PROCEDURAL AND GENERATIVE METHODS FOR GAME CONTENT CREATION

Abstract

References