The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Procedural Content Generation via Machine Learning (PCGML)
Open accessAbstract
This survey explores procedural content generation via machine learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement; this paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver-based, and constructive methods). We focus on what is most often considered functional game content, such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games, as opposed to cosmetic content, such as sprites and sound effects. In addition to using PCG for autonomous generation, cocreativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the generated content. Multiple PCGML methods are covered, including neural networks: long short-term memory networks, autoencoders, and deep convolutional networks; Markov models: $n$-grams and multi-dimensional Markov chains; clustering; and matrix factorization. Finally, we discuss open problems in PCGML, including learning from small data sets, lack of training data, multilayered learning, style-transfer, parameter tuning, and PCG as a game mechanic.
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Cited by
Deep Learning for Video Game Playing
Justesen, N., Bontrager, P., Togelius, J., & Risi, S. (2020) · IEEE Transactions on Games · doi:10.1109/tg.2019.2896986 · Cited by 257
Self-citationPCGRL: Procedural Content Generation via Reinforcement Learning
Khalifa, A., Bontrager, P., Earle, S., & Togelius, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · doi:10.1609/aiide.v16i1.7416 · Cited by 156
Self-citationSchrum, J., Gutierrez, J., Volz, V., Liu, J., Lucas, S., & Risi, S. (2020) · Proceedings of the 2020 Genetic and Evolutionary Computation Conference · doi:10.1145/3377930.3389821 · Cited by 63
Du, Z., & Lu, R. (2025) · Batteries · doi:10.3390/batteries11060204 · Cited by 58
Game Generation via Large Language Models
Hu, C., Zhao, Y., & Liu, J. (2024) · 2024 IEEE Conference on Games (CoG) · doi:10.1109/cog60054.2024.10645597 · Cited by 23
Metadata
- Authors
- Summerville, A., Snodgrass, S., Guzdial, M., Holmgard, C., Hoover, A. K., Isaksen, A., Nealen, A., & Togelius, J.
- Venue
- IEEE Transactions on Games
- Published
- 2018-09
- Volume
- 10
- Issue
- 3
- Pages
- 257-270
- ISSN
- 2475-1502 (ISSN-L)
- 2475-1510
- OpenAlex
- W2586544230
- Cited by
- 425
- References
- 60
- FWCI
- 26.59
- Type
- journal article
- Language
- en
- Open access
- green
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- Summerville, A., Snodgrass, S., Guzdial, M., Holmgard, C., Hoover, A. K., Isaksen, A., Nealen, A., & Togelius, J. (2018). Procedural Content Generation via Machine Learning (PCGML). IEEE Transactions on Games. https://doi.org/10.1109/tg.2018.2846639
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