[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, vol. 141, p. 102465, 2020.

 

[2] A. Latif, M.F. Zuhairi, F.Q., P. Khan Randhawa and A. Patel, “A critical evaluation of procedural content generation approaches for Digital Twins,” Journal of Sensors, 2022(1), p.5629645, 2022.

 

[3] F. Quarneti, “Steam Sets New Concurrent Player Record with Over 37 Million Users,” [Online]. Available:https://www.benzinga.com/general/gaming/24/08/40560450/steam-sets-new-concurrent-player-record-with-over-37-million-users. [Accessed: August. 26, 2024].

 

[4] G. 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.

 

[5] M. 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.

 

[6]‎ A. 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.

 

[7] A. 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.

 

[8] J. 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.

 

[9] Mao, X., Yu, W., Yamada, K.D. and M.R. Zielewski, “Procedural Content Generation via Generative Artificial Intelligence,” arXiv preprint arXiv:2407.09013, 2024.

 

[10] Z. 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.

 

[11] R. Gallotta, G. Todd, M. Zammit, S. Earle, A. Liapis, J. Togelius, and GN. Yannakakis, “Large language models and games: A survey and roadmap,” arXiv preprint arXiv:2402.18659. 2024.

 

[12] J. Freiknecht and W. Effelsberg, “A survey on the procedural generation of virtual worlds,” Multimodal Technologies and Interaction, 1(4), p.27, 2017.

[13] R.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] N.A. Barriga, “A short introduction to procedural content generation algorithms for videogames,” International Journal on Artificial Intelligence Tools, 28(02), p.1930001, 2019.

 

[15] S. Risi, and J. Togelius, “Increasing generality in machine learning through procedural content generation,” Nature Machine Intelligence, 2(8), pp.428-436, 2020.

 

[16] P. Sweetser, “Large language models and video games: A preliminary scoping review,” InProceedings of the 6th ACM Conference on Conversational User Interfaces, pp. 1-8, 2024.

 

[17] M. Hendrikx, S. Meijer, J. Van Der Velden, A. Iosup, “Procedural content generation for games: A survey,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 9(1):1-22. 2013.

 

[18] L.P. Zhuhadar and M.D. Lytras, “The application of AutoML techniques in diabetes diagnosis: current approaches, performance, and future directions,” Sustainability, 15(18), p.13484, 2023.

 

[19] Y. Wu, A. Yi, C. Ma, and L. Chen, “Artificial intelligence for video game visualization, advancements, benefits and challenges,” Mathematical Biosciences and Engineering, 20(8), pp.15345-15373, 2023.

 

[20] J. Liang, Y. Tang, R. Hare, B. Wu, and F.Y. Wang, “A learning-embedded attributed Petri net to optimize student learning in a serious game,” IEEE Transactions on Computational Social Systems, 10(3), pp.869-877. 2021

 

[21] E.A.L. Estolas, A.F.V. Malimban, J.T. Nicasio, Rivera, J.S., M.F.D. San Pablo, and T.L. Takahashi, “Automatic Beatmap Generating Rhythm Game Using Music Information Retrieval with Machine Learning for Genre Detection,” In IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). pp 1-6, 2020.

 

[22] J. Rafner, S. Langsford, A. Hjorth, M. Gajdacz, L. Philipsen, S. Risi, J. Simon, J. Sherson, “Utopian or Dystopian?: using a ML-assisted image generation game to empower the general public to envision the future,” In Creativity and Cognition,  pp 1-5, 2021.

 

[23] T. Shi, Z. Zou, Z. Shi, and Y. Yuan, “Neural rendering for game character auto-creation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(3), pp.1489-1502, 2020.

 

[24] C. Stephens and DC. Exton, “Assessing Multiplayer Level Design Using Deep Learning Techniques,” In Proceedings of the 15th International Conference on the Foundations of Digital Games, pp 1-3, 2020.

 

[25] K. Sorochan, J. Chen, Y. Yu, M. Guzdial, “Generating Lode Runner Levels by Learning Player Paths with LSTMs,” In Proceedings of the 16th International Conference on the Foundations of Digital Games, pp 1-7, 2021.

 

[26] M. Ince, “BiLSTM and dynamic fuzzy AHP-GA method for procedural game level generation,” Neural Computing and Applications, 33(15):9761-73, 2021.

 

[27] S. Andreu and M.V. Aylagas, “Neural synthesis of sound effects using flow-based deep generative models,” In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, Vol. 18, No. 1, pp. 2-9, 2022.

 

[28] A. 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.

 

[29] L. Lazaridis and G.F. Fragulis, “Creating a Newer and Improved Procedural Content Generation (PCG) Algorithm with Minimal Human Intervention for Computer Gaming Development,” Computers, 13(11), p.304, 2024.

 

[30] T. Shi, Z. Zou, X. Song, Z. Song, C. Gu, C. Fan, and Y. Yuan, “Neutral face game character auto-creation via pokerface-gan,” In Proceedings of the 28th ACM International Conference on Multimedia, pp. 3201-3209, 2020.

 

[31] G. Ivanov, MH. Petersen, K. Kovalský, K. Engberg, G. Palamas, “An Explorative Design Process for Game Map Generation Based on Satellite Images and Playability Factors,” In Proceedings of the 15th International Conference on the Foundations of Digital Games, pp. 1-4, 2020.

 

[32] F. Schubert, M. Awiszus, and B. Rosenhahn, “Toad-gan: a flexible framework for few-shot level generation in token-based games,” IEEE Transactions on Games, 14(2):284-93, 2021.

 

[33] A. Hald, JS. Hansen, J. Kristensen, and P. Burelli “Procedural content generation of puzzle games using conditional generative adversarial networks,” In Proceedings of the 15th International Conference on the Foundations of Digital Games, pp. 1-9, 2020.

 

[34] V. Volz, N. Justesen, S. Snodgrass, S. Asadi, S. Purmonen, C. Holmgård, J. Togelius, and S. Risi, “Capturing local and global patterns in procedural content generation via machine learning,” In IEEE Conference on Games (CoG), pp 399-406, 2020.

 

[35] J. 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, 2022.

 

[36] T. Maurer and M. Guzdial, “Adversarial Random Forest Classifier for Automated Game Design,” In Proceedings of the 16th International Conference on the Foundations of Digital Games. pp 1-6, 2021.

 

[37] S. Nam and K. Ikeda, “Generation of diverse stages in turn-based role-playing game using reinforcement learning”, In 2019 IEEE Conference on Games (CoG), pp. 1-8, 2019.

 

[38] Z. Chen, C. Amato, T.H.D. Nguyen, S. Cooper, Y. Sun, and M.S. El-Nasr, “Q-deckrec: A fast deck recommendation system for collectible card games,” In 2018 IEEE conference on Computational Intelligence and Games (CIG), pp. 1-8, 2018.

 

[39] A. Khalifa, P. Bontrager, S. Earle, J. Togelius, “Pcgrl: Procedural content generation via reinforcement learning,” In Proceedings of the AAAI Conference on Artificial Intelligence 2020 and Interactive Digital Entertainment, 16(1):95-101.

 

[40] T. Shu, J. Liu, G.N. Yannakakis, “Experience-driven PCG via reinforcement learning: A Super Mario Bros study,” In 2021 IEEE Conference on Games (CoG), pp 1-9, 2021.

 

[41] Y. Zakaria, M. Fayek, and M Hadhoud, “Procedural level generation for sokoban via deep learning: An experimental study,” IEEE Transactions on Games, 2022.

 

[42] P.V.M. Dutra, S.M. Villela, and R.F. Neto, “A mixed-initiative design framework for procedural content generation using reinforcement learning,” Entertainment Computing, 52, p.100759, 2025.

 

[43] L.J. Martin, “Neurosymbolic Automated Story Generation,” Doctoral dissertation, Georgia Institute of Technology, Atlanta, GA, USA., 2021.

 

[44] C. 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), pp 1-8, 2021.

 

[45] S.G. Nam, C.H. Hsueh, and K. Ikeda, “Generation of Game Stages with Quality and Diversity by Reinforcement Learning in Turn-Based RPG,” IEEE Transactions on Games, 14(3):488-501, 2021.

 

[46] Z. Wang, J. Liu, “Online Game Level Generation from Music,” In 2022 IEEE Conference on Games (CoG), pp. 119-126, 2022.

 

[47] Z. Jiang, S. Earle, M. Green, J. Togelius, “Learning Controllable 3D Level Generators,” In Proceedings of the 17th International Conference on the Foundations of Digital Games, pp. 1-9, 2022.

 

[48] Z. 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.

[49] T. Huber, S. Mertes, S. Rangelova, S. Flutura, and E. André, “Dynamic difficulty adjustment in virtual reality exergames through experience-driven procedural content generation,” In2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1-8, 2021.

 

[50] J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems, 33, pp.6840-6851, 2020.

 

[51] M. Zhang, Z. Cai, L. Pan, F. Hong, X. Guo, L. Yang, and Z. Liu, “Motiondiffuse: Text-driven human motion generation with diffusion model,” arXiv preprint arXiv:2208.15001, 2022.

 

[52] R. Gallotta, A. Liapis, and G. Yannakakis, “LLMaker: A Game Level Design Interface Using (Only) Natural Language,” 2024.

 

[53] S. Luo, and W. Hu, “Diffusion probabilistic models for 3d point cloud generation,” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2837-2845, 2021.

 

[54] A. Vahdat, F. Williams, Z. Gojcic, O. Litany, S. Fidler, and K. Kreis, “Lion: Latent point diffusion models for 3d shape generation,” Advances in Neural Information Processing Systems, 35, pp.10021-10039, 2022.

 

[55] L. Zhou, Y. Du, and J. Wu, “3d shape generation and completion through point-voxel diffusion,” In Proceedings of the IEEE/CVF international conference on computer vision, pp. 5826-5835, 2021.

 

[56] S.W. Kim, B. Brown, K. Yin, K. Kreis, K. Schwarz, D. Li, R. Rombach, A. Torralba, and S. Fidler, “Neuralfield-ldm: Scene generation with hierarchical latent diffusion models,” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 8496-8506, 2023.

 

[57] D. Jurafsky and J. H. Martin, “Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models (3rd Edition),” 2024. [E-book] Available: https://web.stanford.edu/~jurafsky/slp3/.

 

[58] J. Freiknecht and W. Effelsberg, “Procedural generation of interactive stories using language models,” In Proceedings of the 15th International Conference on the Foundations of Digital Games, pp. 1-8, 2020.

 

[59] G. Wang, Y. Xie, Y. Jiang, , A. Mandlekar, C. Xiao, Y. Zhu, L. Fan, and A. Anandkumar, “Voyager: An open-ended embodied agent with large language models,” arXiv preprint arXiv:2305.16291, 2023.

 

[60] J.S. Park, J. O’Brien, C.J. Cai, M.R. Morris, P. Liang, and M.S. Bernstein, “Generative agents: Interactive simulacra of human behavior,” In Proceedings of the 36th annual acm symposium on user interface software and technology, pp. 1-22, 2023.

[61] N. Ranella, and M. Eger, “Towards Automated Video Game Commentary Using Generative AI,” In EXAG@ AIIDE, 2023.

 

[62] M. Hua and R. Raley, “Playing With Unicorns: AI Dungeon and Citizen NLP,” DHQ: Digital Humanities Quarterly, 14(4), 2020.

 

[63] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10684-10695, 2022.

 

[64] Y. Sun, Z. Li, K. Fang, C.H. Lee, and A. Asadipour, “Language as reality: a co-creative storytelling game experience in 1001 nights using generative AI,” In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, Vol. 19, No. 1, pp. 425-434, 2023.

 

[65] L. Huang, and X. Sun, “Create ice cream: Real-time creative element synthesis framework based on gpt3. 0,” In 2023 IEEE Conference on Games (CoG), pp. 1-4, 2023.

 

[66] G. Todd, S. Earle, M.U. Nasir, M.C. Green, and J. Togelius, “Level generation through large language models,” In Proceedings of the 18th International Conference on the Foundations of Digital Games, pp. 1-8, 2023.

 

[67] M.U. Nasir and J. Togelius, “Practical PCG through large language models,” In 2023 IEEE Conference on Games (CoG), pp. 1-4, 2023.

 

[68] R. Gallotta, A. Liapis, and G. Yannakakis, “Consistent Game Content Creation via Function Calling for Large Language Models,” antoniosliapis.com, 2024.