Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Deep learning for procedural content generation

Liu Jialin, Snodgrass Sam, Khalifa Ahmed, Risi Sebastian, Giannakakis Georgios, Togelius, Julian, 1979-

Simple record


URIhttp://purl.tuc.gr/dl/dias/880BAA66-C57B-4192-BB61-AD58EF34DFC1-
Identifierhttps://doi.org/10.1007/s00521-020-05383-8-
Identifierhttps://link.springer.com/article/10.1007/s00521-020-05383-8-
Languageen-
Extent19 pagesen
TitleDeep learning for procedural content generationen
CreatorLiu Jialinen
CreatorSnodgrass Samen
CreatorKhalifa Ahmeden
CreatorRisi Sebastianen
CreatorGiannakakis Georgiosen
CreatorΓιαννακακης Γεωργιοςel
CreatorTogelius, Julian, 1979-en
PublisherSpringer Natureen
Content SummaryProcedural 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.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2021-05-14-
Date of Publication2021-
SubjectProcedural content generationen
SubjectGame designen
SubjectDeep learningen
SubjectMachine learningen
SubjectComputational and artificial intelligenceen
Bibliographic CitationJ. Liu, S. Snodgrass, A. Khalifa, S. Risi, G. N. Yannakakis, and J. Togelius, “Deep learning for procedural content generation,” Neural Comput. & Appl., vol. 33, no. 1, pp. 19–37, Jan. 2021. doi: 10.1007/s00521-020-05383-8en

Services

Statistics