دانشگاه سمنان

 آمار

تعداد نشریات22
تعداد شماره‌ها712
تعداد مقالات10,258
تعداد مشاهده مقاله72,013,224
تعداد دریافت فایل اصل مقاله63,762,210
Saffari, Shaqayeq, Dorrigiv, Morteza, Yaghmaee, Farzin. (1402). Enthusiasms, Challenges, and Future Trends in Procedural Content Generation in Games Using Machine Learning. هنرهای کاربردی, 3(4), 7-22. doi: 10.22075/mseee.2025.32606.1136
Shaqayeq Saffari; Morteza Dorrigiv; Farzin Yaghmaee. "Enthusiasms, Challenges, and Future Trends in Procedural Content Generation in Games Using Machine Learning". هنرهای کاربردی, 3, 4, 1402, 7-22. doi: 10.22075/mseee.2025.32606.1136
Saffari, Shaqayeq, Dorrigiv, Morteza, Yaghmaee, Farzin. (1402). 'Enthusiasms, Challenges, and Future Trends in Procedural Content Generation in Games Using Machine Learning', هنرهای کاربردی, 3(4), pp. 7-22. doi: 10.22075/mseee.2025.32606.1136
Saffari, Shaqayeq, Dorrigiv, Morteza, Yaghmaee, Farzin. Enthusiasms, Challenges, and Future Trends in Procedural Content Generation in Games Using Machine Learning. هنرهای کاربردی, 1402; 3(4): 7-22. doi: 10.22075/mseee.2025.32606.1136

Enthusiasms, Challenges, and Future Trends in Procedural Content Generation in Games Using Machine Learning

Journal of Modeling and Simulation in Electrical and Electronics Engineering
دوره 3، شماره 4 - شماره پیاپی 14، اردیبهشت 2024، صفحه 7-22    اصل مقاله (537.06 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22075/mseee.2025.32606.1136
نویسندگان
Shaqayeq Saffari؛ Morteza Dorrigiv* ؛ Farzin Yaghmaee
Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran.
تاریخ دریافت: 17 آذر 1402،  تاریخ بازنگری: 13 اردیبهشت 1403،  تاریخ پذیرش: 28 بهمن 1403 
چکیده
-- Procedural Content Generation (PCG) in video games, defined as the automated creation of game content through algorithmic processes, has experienced significant evolution over time. This advancement enables the generation of diverse game elements with minimal designer input. Historically, the development of PCG in games has utilized various methodologies, including search-based, solver-based, rule-based, and grammar-based approaches. These techniques have played a crucial role in producing a broad spectrum of content, such as levels, maps, character models, and textures. More recently, the incorporation of artificial intelligence, notably machine learning, has revolutionized the field of content generation within the gaming industry. Leveraging vast datasets and enhanced computational capabilities, machine learning algorithms offer unprecedented opportunities for creating dynamic and immersive game environments. Despite these advancements, a significant lack of comprehensive research thoroughly examines this field from crucial perspectives. This paper analyzes machine learning applications in game content generation, focusing on key issues in the PCG domain, such as enthusiasm, current challenges, and exploring potential future trends.
کلیدواژه‌ها
Artificial intelligence؛ Machine learning؛ Game design؛ Procedural content generation
مراجع
  1. L. Rodrigues, B. Robson and J. Brancher, “Procedural versus human level generation: Two sides of the same coin?,” International Journal of Human-Computer Studies. 141, p. 102465, 2020.
  2. Hafis, H. Tolle, and A. Afif Supianto, “A literature review of empirical evidence on procedural content generation in game-related implementation,” Journal of Information Technology and Computer Science. vol. 4, pp. 308-328, 2019.
  3. Freiknecht and W. Effelsberg, “A survey on the procedural generation of virtual worlds,” Multimodal Technologies and Interaction. 1(4), p.27, 2017.
  4. Sacco, A. Liapis, and G. N. Yannakakis, “A holistic approach for semantic-based game generation,” In 2016 IEEE Conference on Computational Intelligence and Games (CIG). pp:1-8, 2016.
  5. M. Gilbert Nwankwo and J. Fiaidhi, “Procedural Content Generation for Dynamic Level Design and Difficulty in a 2D Game Using UNITY,” International Journal of Multimedia and Ubiquitous Engineering. 12(9), pp. 41-52, 2017.
  6. Smith, "An Analysis of the Role of Procedural Content Generation in Game Design," Conference on Human Factors in Computing Systems – Proceedings, 2013.
  7. Summerville, S. Snodgrass, M. Guzdial, C. Holmg˚ard, A. K. Hoover, A. Isaksen, A. Nealen, and J. Togelius, “Procedural content generation via machine learning (pcgml),” IEEE Transactions on Games,.vol. 10, pp. 257–270, 2018.
  8. Sarkar and S. Cooper, “Generating and blending game levels via quality-diversity in the latent space of a variational autoencoder,” in The 16th International Conference on the Foundations of Digital Games (FDG), pp. 1–11, 2021.
  9. Liu, S. Snodgrass, A. Khalifa, S. Risi, G. N. Yannakakis, and J. Togelius, “Deep learning for procedural content generation,” Neural Computing and Applications. 33(1), pp. 19-37, 2021.
  10. Nam and K. Ikeda, “Generation of diverse stages in turn-based roleplaying game using reinforcement learning,” in 2019 IEEE Conference on Games (CoG), IEEE, pp. 1–8, 2019.
  11. Zhou and M. Guzdial, “Toward co-creative dungeon generation via transfer learning,” in the 16th International Conference on the Foundations of Digital Games (FDG), pp. 1–9, 2021.
  12. Gonz´alez-Hermida, E. Costa-Montenegro, B. Leger´en-Lago, and A. Pena-Gim´enez, S”tudy of artificial intelligent algorithms applied in procedural content generation in video games,” Eludamos. Journal for Computer Game Culture. 10(1), pp. 39–54, 2020.
  13. L. Cabrera, M.N. Collazo, C.C. Porras and A.J.F. Leiva, “Procedural content generation for real-time strategy games,” IJIMAI. vol. 3, pp. 40-48, 2015.
  14. A. Barriga, “A short introduction to procedural content generation algorithms for videogames,” International Journal on Artificial Intelligence Tools, 28(02), p.1930001, 2019.
  15. Risi, and J. Togelius, “Increasing generality in machine learning through procedural content generation,” Nature Machine Intelligence, 2(8), pp.428-436, 2020.
  16. Hendrikx, S. Meijer, J. Van Der Velden, and A. Iosup, “Procedural content generation for games: A survey,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 9(1), pp. 1–22, 2013.
  17. Janiesch, P. Zschech, K. Heinrich, “Machine learning and deep learning,” Electronic Markets, 31(3), pp.685-95, 2021.
  18. Sarkar, and S. Cooper, “Sequential segment-based level generation and blending using variational autoencoders,” In Proceedings of the 15th International Conference on the Foundations of Digital Games, pp:1-9, 2020.
  19. Georgiou and Y. Demiris, “Adaptive user modeling in car racing games using behavioural and physiological data,” User Modeling and User-Adapted Interaction. vol. 27, pp. 267–311, 2017.
  20. Bellini, Mattia. "Towards a Model to Meet Players’ Preferences in Games," GHITALY@ CHItaly. 2019.
  21. Fang, P. Paliyawan, R. Thawonmas, and T. Harada, “Towards an angry-birds-like game system for promoting mental well-being of players using art-therapy-embedded procedural content generation,” in 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), IEEE, pp. 947–948, 2019.
  22. Li, R. Chen, Y. Xue, R. Wang, W. Li, and M. Guzdial, “Ensemble learning for mega man level generation,” in The 16th International Conference on the Foundations of Digital Games (FDG), pp. 1– 9, 2021.
  23. Osborn, A. Summerville, and M. Mateas, “Automatic mapping of nes games with mappy,” in Proceedings of the 12th International Conference on the Foundations of Digital Games, pp. 1–9, 2017.
  24. Karth, B. Aytemiz, R. Mawhorter, and A. M. Smith, “Neurosymbolic map generation with vq-vae and wfc,” in The 16th International Conference on the Foundations of Digital Games (FDG), pp. 1–6, 2021.
  25. Freiknecht and W. Effelsberg, “Procedural generation of interactive stories using language models,” in International Conference on the Foundations of Digital Games, pp. 1–8, 2020.
  26. Mohaghegh, M. A. R. Dehnavi, G. Abdollahinejad, and M. Hashemi, “PCGPT: Procedural Content Generation via Transformers,” arXiv preprint arXiv:2310.02405,‏ 2023.
  27. Snodgrass S. Sarkar, “A Multi-domain level generation and blending with sketches via example-driven bsp and variational autoencoders,” In Proceedings of the 15th International Conference on the foundations of digital games, pp 1-11, 2020.
  28. Sarkar and S. Cooper, “Sequential segment-based level generation
    and blending using variational autoencoders,” in International Con-
    conference on the Foundations of Digital Games    , pp. 1–9, 2020.
  29. Thakkar, C. Cao, L. Wang, T. J. Choi, and J. Togelius, “Autoencoder and evolutionary algorithm for level generation in lode runner,” in 2019 IEEE Conference on Games (CoG), IEEE, pp. 1–4, 2019.
  30. Mori, A. Shinya, T. Harada, and R. Thawonmas, “Feature extraction of game plays for procedural play generation,” in 2017 Nicograph International (NicoInt), IEEE, pp. 86–86, 2017.
  31. Volz, J. Schrum, J. Liu, S. M. Lucas, A. Smith, and S. Risi, “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, 2018.
  32. Karp and Z. Swiderska-Chadaj, “Automatic generation of graphical game assets using gan,” in 201 7th International Conference on Computer Technology Applications, pp. 7–12, 2021.
  33. J. Chelliah, V. K. Vallabhaneni, S. R. Lenkala, M. J, and M. K. K. Reddy, “3d character generation using pcgml,” International Journal of Innovative Technology and Exploring Engineering (IJITEE). vol. 8, pp.2278-3075, 2019.
  34. Volz, N. Justesen, S. Snodgrass, S. Asadi, S. Purmonen, C. Holmg˚ard, J. Togelius, and S. Risi, “Capturing local and global patterns in procedural content generation via machine learning,” in 2020 IEEE Conference on Games (CoG), IEEE, pp. 399–406, 2020.
  35. Ivanov, M. H. Petersen, K. Kovalsk`y, K. Engberg, and G. Palamas, “An explorative design process for game map generation based on satellite images and playability factors,” in International Conference on the Foundations of Digital Games, pp. 1–4, 2020.
  36. Volz, J. Schrum, J. Liu, S. M. Lucas, A. Smith, and S. Risi, “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, 2018.
  37. Irfan, A. Zafar, and S. Hassan, “Evolving levels for general games using deep convolutional generative adversarial networks,” in 2019 11th Computer Science and Electronic Engineering (CEEC), IEEE, pp. 96–101, 2019.
  38. Schrum, B. Capps, K. Steckel, V. Volz, and S. Risi, “Hybrid encoding for generating large scale game level patterns with local variations,” IEEE Transactions on Games. 15(1), pp. 46-55, 2022.
  39. Ciampiconi, A. Elwood, M. Leonardi, A. Mohamed, and A. Rozza, “A survey and taxonomy of loss functions in machine learning,” arXiv preprint arXiv:2301.05579,‏ 2023.‏
  40. Nam and K. Ikeda, “Generation of diverse stages in turn-based roleplaying game using reinforcement learning,” in 2019 IEEE Conference on Games (CoG), IEEE, pp. 1–8, 2019.
  41. Khalifa, P. Bontrager, S. Earle, and J. Togelius, “Pcgrl: Procedural content generation via reinforcement learning,” in Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 95–10, 2020.
  42. Shu, J. Liu, and G. N. Yannakakis, “Experience-driven PCG via reinforcement learning: A super Mario bros study,” in 2021 IEEE Conference on Games (CoG), IEEE, pp. 1–9, 2021.
  43. Liu, L. Chaoran, L. Yue, M. Heng, H. Xiao, S. Yiming, W. Li-
    cong, C. Ze, G. Xianghao, L. Hengtong, et al., “Automatic generation
    of tower defense levels using PCG,” in Proceedings of the 14th Inter--
    national Conference on the Foundations of Digital Games    , pp.1–9, 2019.
  44. Holmg˚ard, M. C. Green, A. Liapis, and J. Togelius, “Automated playtesting with procedural personas through mcts with evolved heuristics,” IEEE Transactions on Games. 11(4), pp. 352–362, 2018.
  45. Gamage, V. Pinto, C. Xue, M. Stephenson, P. Zhang, and J. Renz, “Novelty generation framework for AI agents in angry birds style physics games,” in 2021 IEEE Conference on Games (CoG), IEEE, pp. 1–8, 2021.
  46. Flimmel, J. Gemrot, and V.Černý, “Coevolution of AI and Level Generators for Super Mario Game,” In 2021 IEEE Congress on Evolutionary Computation (CEC), pp:2093-2100, 2021.
  47. Zhou and M. Guzdial, “Toward co-creative dungeon generation via transfer learning,” in the 16th International Conference on the Foundations of Digital Games (FDG), pp. 1–9, 2021.
  48. J. Kozerawski, “Meta-learning for few-shot image classification,” University of California, Santa Barbara,‏ 2021.‏
  49. Goadrich and J. Droscha, “Improving solvability for procedurally generated challenges in physical solitaire games through entangled components,” IEEE Transactions on Games. vol. 12, pp. 260–269, 2019.
  50. Sirota, V. Bulitko, M. R. Brown, and S. P. Hernandez, “Towards procedurally generated languages for non-playable characters in video games,” in 2019 IEEE Conference on Games (CoG), IEEE, pp. 1–4, 2019.
  51. Karavolos, A. Liapis, and G. N. Yannakakis, “Using a surrogate model of gameplay for automated level design,” in 2018 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp. 1–8, 2018.
  52. Sorochan, J. Chen, Y. Yu, and M. Guzdial, “Generating lode runner levels by learning player paths with lstms,” in the 16th International Conference on the Foundations of Digital Games (FDG), pp. 1–7, 2021.
  53. Liang, W. Li, and K. Ikeda, “Procedural content generation
    of rhythm games using deep learning methods,” in Joint Interna-
    National Conference on Entertainment Computing and Serious Games,
    Springer    , pp. 134–145, 2019.
  54. Tanabe, K. Fukuchi, J. Sakuma, and Y. Akimoto, “Level generation for angry birds with sequential vae and latent variable evolution,” in Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1052–1060, 2021.
  55. Zakaria, M.Fayek, and M. Hadhoud, “Procedural level generation for Sokoban via deep learning: An experimental study,” IEEE Transactions on Games, 15(1), pp:108-120, 2022.
  56. A. Brown, B. Lutfullin, P. Oreshin and I. Pyatkin, “Levels for hotline Miami 2: Wrong number using procedural content generations,” Computers. 7(2), pp. 22, 2018.
  57. Smith, “Understanding procedural content generation: a design-centric analysis of the role of PCG in games,” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 917-926, 2014.
  58. Goandy, “No Escape: A 2D Top-Down Shooting Roguelike Game Embedded with Drunkard Walk Algorithm,” International Journal of Advanced Trends in Computer Science and Engineering. 9(2), pp. 1045-1049, 2020.
  59. Hooshyar, M. Yousefi and M. Wang, “A data‐driven procedural‐content‐generation approach for educational games,” Journal of Computer Assisted Learning. 34(6), pp. 731-739, 2018.
  60. E. Merrick, A. Isaacs, M. Barlow and N. Gu, “A shape grammar approach to computational creativity and procedural content generation in massively multiplayer online role-playing games,” Entertainment Computing. 4(2), pp.115-130, 2013.
  61. Dong and T. Barnes, “Evaluation of a template-based puzzle generator for an educational programming game,” In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, vol. 13, pp.172-178, 2017.
  62. Petrovas and R. Bausys, “Procedural Video Game Scene Generation by Genetic and Neutrosophic WASPAS Algorithms,” Applied Sciences. 12(2), p.772, 2022.
  63. F. Maia, W. Viana and F. Trinta, “Transposition of location-based games: using procedural content generation to deploy balanced game maps to multiple locations,” Pervasive and Mobile Computing. vol.70, p.101302, 2021.
  64. F. Capasso-Ballesteros and De.la. Rosa-Rosero, “Semiautomatic construction of video game design prototypes with MaruGen,” Revista Facultad de Ingeniería Universidad de Antioquia, vol. 99, PP.9-20, 2021.
  65. Isaksen, C. Holmg˚ard, J. Togelius, “Semantic hashing for video game levels,” Game & Puzzle Design, 3(1), pp:10–16, 2017.
  66. Karavolos, A. Liapis, G. Yannakakis, “Learning the patterns of balance in a multi-player shooter game,” In: Proceedings of the 12th International Conference on the Foundations of Digital Games, pp:1–10, 2017.
  67. Min, EY Ha, J. Rowe, B. Mott, J. Lester “Deep learning-based goal recognition in open-ended digital games,” In: Tenth Artificial Intelligence and Interactive Digital Entertainment Conference, 2014.
  68. F. Gudmundsson, P. Eisen, E. Poromaa, A. Nodet, S. Purmonen, B. Kozakowski, R. Meurling, and L. Cao, “Human-like playtesting with deep learning,” In: 2018 IEEE Conference on Computational Intelligence and Games (CIG), IEEE, pp:1–8, 2018.
  69. J. Guzdial, N. Sturtevant, and B. Li, “Deep static and dynamic level analysis: A study on infinite Mario,” In: Twelfth Artificial Intelligence and Interactive Digital Entertainment Conference, 2016.
  70. Holmg˚ard, A. Liapis, J. Togelius, and G.N. Yannakakis “Evolving personas for player decision modeling,” In: 2014 IEEE Conference on Computational Intelligence and Games, IEEE, pp 1–8, 2014.
  71. Smith, “An Analog History of Procedural Content Generation,” In FDG. 2015.
  72. Wang, J. Liu, and G.N. Yannakakis, “The Fun Facets of Mario: Multifaceted Experience-Driven PCG via Reinforcement Learning,” In Proceedings of the 17th International Conference on the Foundations of Digital Games, pp 1-8, 2022.
  73. T. Pereira, P.V. de Souza Prado and R.M. Lopes, “Procedural generation of dungeons’ maps and locked-door missions through an evolutionary algorithm validated with players,” Expert Systems with Applications. vol. 180, p.115009, 2021.
  74. A. Muslim, E.M.A. Jonemaro and M.A. Akbar, “Penerapan Procedural Content Generation untuk Perancangan Level pada 2D Endless Runner Game menggunakan Genetic Algorithm,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer. 3(5), p. 4406-14, 2019.
  75. Doran, “Procedural generation of content for online role playing games,” University of North Texas, 2014.
  76. Chia, "The artist and the automaton in digital game production", Convergence, 2022.
  77. M. Abuzuraiq, O. Alsalman and H. Erhan, “Shopping for Game levels: A Visual Analytics Approach to Exploring Procedurally Generated Content,” In International Conference on the Foundations of Digital Games, pp. 1-4, 2020.
  78. J. Smith and J.J. Bryson, “A logical approach to building dungeons: Answer set programming for hierarchical procedural content generation in roguelike games,” In Proceedings of the 50th Anniversary Convention of the AISB, 2014.
  79. Dey and J. Konert, “Content Generation for Serious Games,” In Entertainment Computing and Serious Games, pp. 174-188, 2016.
  80. Freiberg, “Procedural generation of content in video games,” Ph.D. dissertation, Hochschule für angewandte Wissenschaften Hamburg, 2016.
  81. A. BIYIK and E.L.İ. F.Sürer, “Developing a Space Syntax-Based Evaluation Method for Procedurally Generated Game Levels,” Mugla Journal of Science and Technology. 6(2), pp. 79-88, 2020.
  82. Mehm, A. Hendrich, D. Kauth and S. Göbel, “Procedural Content Generation in Educational Game Authoring Tools,” In ECGBL2014-8th European Conference on Games Based Learning: ECGBL2014, p. 380, 2014.
  83. Mawhorter, B. Aytemiz, I. Karth and A. Smith, “Content reinjection for super Metroid,” In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, vol. 17, pp. 172-178, 2021.
  84. Karavolos, A. Bouwer and R. Bidarra, “Mixed-Initiative Design of Game Levels: Integrating Mission and Space into Level Generation,” In FDG. 2015.
  85. Mobramaein, and J. Whitehead, “A methodology for designing natural language interfaces for procedural content generation,” In Proceedings of the 14th International Conference on the Foundations of Digital Games, pp. 1-9, 2019.
آمار
تعداد مشاهده مقاله: 716
تعداد دریافت فایل اصل مقاله: 468


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب