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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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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
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
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Deep learning for procedural content generation

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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

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
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
Chen QGan JBellucci AJacucci G(2024)"I Felt Everyone Was a Streamer": An Empirical Study on What Makes Avatar Collective Streaming EngagingProceedings of the ACM on Human-Computer Interaction10.1145/36373448:CSCW1(1-25)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637344
Whitney CNorman J(2024)Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent CircumventionProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659002(1733-1744)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3659002
Li YBai LMao YPeng XZhang ZChan ALi JTong XLC R(2024)Affecting Audience Valence and Arousal in 360 Immersive Environments: How Powerful Neural Style Transfer Is?Virtual, Augmented and Mixed Reality10.1007/978-3-031-61041-7_15(224-243)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1007/978-3-031-61041-7_15
Sudhakaran SGonzález-Duque MFreiberger MGlanois CNajarro ERisi SOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)MarioGPTProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668483(54213-54227)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668483
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