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review-article

Deep learning for procedural content generation

Authors:

Sebastian Risi,

Georgios N. Yannakakis,

Julian TogeliusAuthors Info & Claims

Neural Computing and Applications, Volume 33, Issue 1

Pages 19 - 37

https://doi.org/10.1007/s00521-020-05383-8

Published: 01 January 2021 Publication History

Abstract

Procedural content generation in video games has a long history. Existing procedural content generation methods, such as search-based, solver-based, rule-based and grammar-based methods have been applied to various content types such as levels, maps, character models, and textures. A research field centered on content generation in games has existed for more than a decade. More recently, deep learning has powered a remarkable range of inventions in content production, which are applicable to games. While some cutting-edge deep learning methods are applied on their own, others are applied in combination with more traditional methods, or in an interactive setting. This article surveys the various deep learning methods that have been applied to generate game content directly or indirectly, discusses deep learning methods that could be used for content generation purposes but are rarely used today, and envisages some limitations and potential future directions of deep learning for procedural content generation.

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Fukaya KDaylamani-Zad DAgius H(2025)Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the ArtACM Computing Surveys10.1145/370849957:5(1-38)Online publication date: 9-Jan-2025
https://dl.acm.org/doi/10.1145/3708499
Guzdial MLarson K(2024)Human-AI interaction generationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/977(8529-8534)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/977
Maleki MZhao RCardona-Rivera RCooper S(2024)Procedural content generation in gamesProceedings of the Twentieth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment10.1609/aiide.v20i1.31877(167-178)Online publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1609/aiide.v20i1.31877
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Deep learning for procedural content generation

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 33, Issue 1

Jan 2021

484 pages

ISSN:0941-0643

EISSN:1433-3058

Issue’s Table of Contents

© Springer-Verlag London Ltd., part of Springer Nature 2020.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2021

Accepted: 23 September 2020

Received: 14 May 2020

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Funding Sources

National Key R&D Program of China
National Natural Science Foundation of China
Guangdong Provincial Key Laboratory
Program for Guangdong Introducing Innovative and Entrepreneurial Teams
Science and Technology Innovation Committee Foundation of Shenzhen
Shenzhen Science and Technology Program
Program for University Key Laboratory of Guangdong Province
Google Faculty Research award
National Science Foundation

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Cited By

Fukaya KDaylamani-Zad DAgius H(2025)Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the ArtACM Computing Surveys10.1145/370849957:5(1-38)Online publication date: 9-Jan-2025
https://dl.acm.org/doi/10.1145/3708499
Guzdial MLarson K(2024)Human-AI interaction generationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/977(8529-8534)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/977
Maleki MZhao RCardona-Rivera RCooper S(2024)Procedural content generation in gamesProceedings of the Twentieth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment10.1609/aiide.v20i1.31877(167-178)Online publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1609/aiide.v20i1.31877
Dai SZhu XLi NDai TWang ZWooldridge MDy JNatarajan S(2024)Procedural level generation with diffusion models from a single exampleProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i9.28865(10021-10029)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i9.28865
Zhang KChen YYang XZhang WJin CWooldridge MDy JNatarajan S(2024)Point cloud part editingProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i7.28547(7187-7195)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i7.28547
Zamorano MDomingo ÁCetina CSarro F(2024)Game Software Engineering: A Controlled Experiment Comparing Automated Content Generation TechniquesProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686690(302-313)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3674805.3686690
Franceschelli GMusolesi M(2024)Creativity and Machine Learning: A SurveyACM Computing Surveys10.1145/366459556:11(1-41)Online publication date: 28-Jun-2024
https://dl.acm.org/doi/10.1145/3664595
Withington OCook MTokarchuk L(2024)On the Evaluation of Procedural Level Generation SystemsProceedings of the 19th International Conference on the Foundations of Digital Games10.1145/3649921.3650016(1-10)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3649921.3650016
Earle SKokkinos FNie YTogelius JRaileanu R(2024)DreamCraft: Text-Guided Generation of Functional 3D Environments in MinecraftProceedings of the 19th International Conference on the Foundations of Digital Games10.1145/3649921.3649943(1-15)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3649921.3649943
Chueca JBlasco DCetina CFont J(2024)Leveraging Phylogenetics in Software Product Families: The Case of Latent Content Generation in Video GamesProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3672596(113-124)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3646548.3672596
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