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DC Field | Value | Language |
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dc.contributor.author | Liu, Jialin | - |
dc.contributor.author | Snodgrass, Sam | - |
dc.contributor.author | Khalifa, Ahmed | - |
dc.contributor.author | Risi, Sebastian | - |
dc.contributor.author | Yannakakis, Georgios N. | - |
dc.contributor.author | Togelius, Julian | - |
dc.date.accessioned | 2021-09-07T10:26:04Z | - |
dc.date.available | 2021-09-07T10:26:04Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Liu, J., Snodgrass, S., Khalifa, A., Risi, S., Yannakakis, G. N., & Togelius, J. (2021). Deep learning for procedural content generation. Neural Computing and Applications, 33, 19-37. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/80826 | - |
dc.description.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. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Springer UK | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Level design (Computer science) | en_GB |
dc.subject | Computer games -- Design | en_GB |
dc.subject | Computational intelligence | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.subject | Machine learning | en_GB |
dc.title | Deep learning for procedural content generation | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1007/s00521-020-05383-8 | - |
dc.publication.title | Neural Computing and Applications | en_GB |
Appears in Collections: | Scholarly Works - InsDG |
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Deep_learning_for_procedural_content_generation_2021.pdf Restricted Access | 2.04 MB | Adobe PDF | View/Open Request a copy |
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