Procedural Map Generation by Energy Propagation

• Mauro Victor Pimentel Dias UFC
• Artur de Oliveira da Rocha Franco UFC
• Jose Wellington Franco da Silva UFC
• José Gilvan Rodrigues Maia UFC

DOI: https://doi.org/10.5753/sbgames.2025.10173

Resumo

Introduction: Procedural map generation plays a key role in popular game genres such as role-playing games and roguelikes, adding variety and dynamicity that leverages replayability or assists in level design tasks. Objective: We introduce a novel algorithm based on energy propagation for generating maps consisting of connected cells (rooms). Methodology or Steps: Starting from the concept of energy propagation, the algorithm simulates energy distribution across multiple rooms connecting an origin cell to a destination cell. We carried out an extensive experimental evaluation of this algorithm using a two-dimensional grid layout. Results: The algorithm executes in linear time on the number of cells, making it efficient enough to be applied in realtime scenarios. The proposed algorithm can be controlled to yield variable structures, ranging from tree layouts to fully interconnected grids.

Palavras-chave: PCG, map generation, dungeon, energy propagation, roguelike

Referências

da Rocha Franco, A. d. O., da Silva, J. W. F., Maia, J. G. R., e de Castro, M. F. (2023). Harnessing generative grammars and genetic algorithms for immersive 2d maps. Entertainment Computing, 47:100595.

da Rocha Franco, A. d. O., Franco, W., Maia, J. G. R., e Franklin, M. (2022). Generating rooms using generative grammars and genetic algorithms. In 2022 21st Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), pages 1–6. IEEE.

Grossmann, L. (2024). A literature review on the educational use of procedural content generation across disciplines.

Liu, J., Snodgrass, S., Khalifa, A., Risi, S., Yannakakis, G. N., e Togelius, J. (2021). Deep learning for procedural content generation. Neural Computing and Applications, 33(1):19–37.

Nasir, M. U. e Togelius, J. (2023). Practical pcg through large language models. In 2023 IEEE Conference on Games (CoG), pages 1–4.

Parker, R. (2017). The culture of permadeath: Roguelikes and terror management theory. Journal of Gaming & Virtual Worlds, 9(2):123–141.

Shaker, N., Togelius, J., e Nelson, M. J. (2016). Procedural content generation in games. Springer.

Silva, D. F., Torchelsen, R. P., e Aguiar, M. S. (2025). Procedural game level generation with gans: potential, weaknesses, and unresolved challenges in the literature. Multimedia Tools and Applications, pages 1–27.

Smith, M. (2024). 6 earliest games that used procedural generation. [link].

So, A. R. P., Souza, A. C. C., Costa, L. M., Mantovani, R. G., e Souza, F. C. M. (2024). Design and evaluation of a procedurally generated dungeon game. In Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames), pages 329–339. SBC.

Summerville, A., Snodgrass, S., Guzdial, M., Holmgård, C., Hoover, A. K., Isaksen, A., Nealen, A., e Togelius, J. (2018). Procedural content generation via machine learning (pcgml). IEEE Transactions on Games, 10(3):257–270.

Togelius, J., Shaker, N., e Dormans, J. (2016). Grammars and l-systems with applications to vegetation and levels. In Procedural Content Generation in Games, pages 73–98. Springer.

Viana, B. M. e dos Santos, S. R. (2019). A survey of procedural dungeon generation. In 2019 18th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), pages 29–38. IEEE.

Werneck, M. e Clua, E. W. (2020). Generating procedural dungeons using machine learning methods. In 2020 19th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), pages 90–96. IEEE.

Zhou, M., Wang, Y., Hou, J., Zhang, S., Li, Y., Luo, C., Peng, J., e Zhang, Z. (2025). Scenex: Procedural controllable large-scale scene generation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 10806–10814.