Procedural Content Generation via Machine Learning (PCGML)

By A. Summerville, Sam Snodgrass, Matthew J. Guzdial, Christoffer Holmgård, Amy K. Hoover, Aaron Isaksen, Andy Nealen, Julian Togelius. IEEE Transactions on Games, 2017

463 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/c5c3fa019574988c479474c32f34debecd12e8d1

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Citations in the corpus

Reinforcement Learning for Improving Agent Design (2018, methodology): “nstrain the characters to keep the essence of their original forms. Optimizing character design may complement existing work on machine learning-assisted procedural content generation for game design [19, 47, 49, 51]. By framing the approach within the popular OpenAI Gym framework, design ﬁrms can c”
Increasing generality in machine learning through procedural content generation (2019, methodology): “[1, 15, 16] PCGML with constrained sampling [17] Standard search-based [18, 19, 20, 21, 22] Latent Variable Evolution [23, 24] PCGRL [25] Generative Playing Networks [26] Activation Maximization [27] Simple Data Augmentation [8, 9] Uniform Domain Randomization [10] Guided Domain Randomization [28]…”
Genie: Generative Interactive Environments (2024, background): “…is also re-lated to Procedural Content Generation (PCG, e.g. Risi and Togelius, 2020a,b) where machine learning has proven highly effective for generating game levels (Summerville et al., 2018), recently via language models that directly write game code (Sudhakaran et al., 2023; Todd et al., 2023).”
Winning Isn't Everything: Training Human-Like Agents for Playtesting and Game AI (2019, background): “See [30], [31] for a survey of these techniques in game design.”