Abstract
Methods of algorithmic data generation, also known as Procedural Content Generation (PCG), consist of a striking vision within the gaming development industry. It is a way of creating enormously unique and diverse content, something that exponentially increases the game replayability. Although PCG in video games has a long history, there are also plenty of methods that have already been applied to levels, maps, models and textures among others. There is a variety of methods that have been used in video games, each with its own advantages and disadvantages. In the current study, an algorithm which generates 2D maps filled with rooms and some decorating items is presented. Map generation in commercial games heavily relies on constructive algorithms which do not evaluate and regenerate the output if something goes wrong. They do not demand heavy processing power and they can be used in real time situations, such as generating big worlds with fauna and flora. However, the playability of the generated map is examined by an agent which is usually created to access every corridor, room, and the start to finish pathway.
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References
Adams, C., Louis, S.: Procedural maze level generation with evolutionary cellular automata. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE (2017)
Alvarez, A., Dahlskog, S., Font, J., Togelius, J.: Empowering quality diversity in dungeon design with interactive constrained map-elites. In: 2019 IEEE Conference on Games (CoG), pp. 1–8. IEEE (2019)
Antoniuk, I., Rokita, P.: Procedural generation of multilevel dungeons for application in computer games using schematic maps and L-system. In: Bembenik, R., Skonieczny, Ł, Protaziuk, G., Kryszkiewicz, M., Rybinski, H. (eds.) Intelligent Methods and Big Data in Industrial Applications. SBD, vol. 40, pp. 261–275. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77604-0_19
Bontrager, P., Togelius, J.: Learning to generate levels from nothing. In: 2021 IEEE Conference on Games (CoG), pp. 1–8. IEEE (2021)
De Kegel, B., Haahr, M.: Procedural puzzle generation: a survey. IEEE Trans. Games 12(1), 21–40 (2019)
Delarosa, O., Dong, H., Ruan, M., Khalifa, A., Togelius, J.: Mixed-initiative level design with RL brush. In: Romero, J., Martins, T., Rodríguez-Fernández, N. (eds.) EvoMUSART 2021. LNCS, vol. 12693, pp. 412–426. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72914-1_27
McMillen, E., Himsl, F.: The Binding of Isaac. https://bindingofisaac.fandom.com
Electronic-Arts: Apex legends. https://www.ea.com/games/apex-legends
Flores-Aquino, G.O., Ortega, J.D.D., Arvizu, R.Y.A., Muñoz, R.L., Gutierrez-Frias, O.O., Vasquez-Gomez, J.I.: 2D grid map generation for deep-learning-based navigation approaches. arXiv preprint arXiv:2110.13242 (2021)
de Freitas, V.M.R.: Procedural generation of cave-like maps for 2D top-down games (2021)
Gellel, A., Sweetser, P.: A hybrid approach to procedural generation of roguelike video game levels. In: International Conference on the Foundations of Digital Games, pp. 1–10 (2020)
Gisslén, L., Eakins, A., Gordillo, C., Bergdahl, J., Tollmar, K.: Adversarial reinforcement learning for procedural content generation. In: 2021 IEEE Conference on Games (CoG), pp. 1–8. IEEE (2021)
Gravina, D., Khalifa, A., Liapis, A., Togelius, J., Yannakakis, G.N.: Procedural content generation through quality diversity. In: 2019 IEEE Conference on Games (CoG), pp. 1–8. IEEE (2019)
Green, M.C., Khalifa, A., Alsoughayer, A., Surana, D., Liapis, A., Togelius, J.: Two-step constructive approaches for dungeon generation. In: Proceedings of the 14th International Conference on the Foundations of Digital Games, pp. 1–7 (2019)
Gutierrez, J., Schrum, J.: Generative adversarial network rooms in generative graph grammar dungeons for the legend of Zelda. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2020)
Khalifa, A., Bontrager, P., Earle, S., Togelius, J.: PCGRL: procedural content generation via reinforcement learning. In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, vol. 16, pp. 95–101 (2020)
Lai, G., Latham, W., Leymarie, F.F.: Towards friendly mixed initiative procedural content generation: three pillars of industry. In: International Conference on the Foundations of Digital Games, pp. 1–4 (2020)
Lazaridis, L., Papatsimouli, M., Kollias, K.-F., Sarigiannidis, P., Fragulis, G.F.: Hitboxes: a survey about collision detection in video games. In: Fang, X. (ed.) HCII 2021. LNCS, vol. 12789, pp. 314–326. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77277-2_24
Liapis, A.: 10 years of the PCG workshop: past and future trends. In: International Conference on the Foundations of Digital Games, pp. 1–10 (2020)
Liu, J., Snodgrass, S., Khalifa, A., Risi, S., Yannakakis, G.N., Togelius, J.: Deep learning for procedural content generation. Neural Comput. Appl. 33(1), 19–37 (2020). https://doi.org/10.1007/s00521-020-05383-8
Minini, P., Assuncao, J.: Combining constructive procedural dungeon generation methods with wavefunctioncollapse in top-down 2D games. In: Proceedings of SBGames (2020)
Persson, M.: Minecraft. https://www.minecraft.net/en-us
Snodgrass, S., Ontanón, S.: Learning to generate video game maps using Markov models. IEEE Trans. Comput. Intell. AI Games 9(4), 410–422 (2016)
Song, A., Whitehead, J.: TownSim: agent-based city evolution for naturalistic road network generation. In: Proceedings of the 14th International Conference on the Foundations of Digital Games, pp. 1–9 (2019)
Summerville, A.: Expanding expressive range: evaluation methodologies for procedural content generation. In: Fourteenth Artificial Intelligence and Interactive Digital Entertainment Conference (2018)
Summerville, A., et al.: Procedural content generation via machine learning (PCGML). IEEE Trans. Games 10(3), 257–270 (2018)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)
Thompson, T., Lavender, B.: A generative grammar approach for action-adventure map generation in the legend of Zelda (2017)
Torrado, R.R., Khalifa, A., Green, M.C., Justesen, N., Risi, S., Togelius, J.: Bootstrapping conditional GANs for video game level generation. In: 2020 IEEE Conference on Games (CoG), pp. 41–48. IEEE (2020)
Viana, B.M., dos Santos, S.R.: Procedural dungeon generation: a survey. J. Interact. Syst. 12(1), 83–101 (2021)
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Lazaridis, L., Kollias, KF., Maraslidis, G., Michailidis, H., Papatsimouli, M., Fragulis, G.F. (2022). Auto Generating Maps in a 2D Environment. In: Fang, X. (eds) HCI in Games. HCII 2022. Lecture Notes in Computer Science, vol 13334. Springer, Cham. https://doi.org/10.1007/978-3-031-05637-6_3
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