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DC Field | Value | Language |
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dc.contributor.author | Rodriguez Torrado, Ruben | - |
dc.contributor.author | Khalifa, Ahmed | - |
dc.contributor.author | Cerny Green, Michael | - |
dc.contributor.author | Justesen, Niels | - |
dc.contributor.author | Risi, Sebastian | - |
dc.contributor.author | Togelius, Julian | - |
dc.date.accessioned | 2021-10-12T05:32:32Z | - |
dc.date.available | 2021-10-12T05:32:32Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Rodriguez Torrado, R., Khalifa, A., Cerny Green, M., Justesen, N., Risi, S., & Togelius, J. (2020). Bootstrapping conditional GANs for video game level generation. 2020 IEEE Conference on Games (CoG), Osaka. 41-48. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/81987 | - |
dc.description.abstract | Generative Adversarial Networks (GANs) have shown impressive results for image generation. However, GANs face challenges in generating contents with certain types of constraints, such as game levels. Specifically, it is difficult to generate levels that have aesthetic appeal and are playable at the same time. Additionally, because training data usually is limited, it is challenging to generate unique levels with current GANs. In this paper, we propose a new GAN architecture named Conditional Embedding Self-Attention Generative Adversarial Net-work (CESAGAN) and a new bootstrapping training procedure. The CESAGAN is a modification of the self-attention GAN that incorporates an embedding feature vector input to condition the training of the discriminator and generator. This allows the network to model non-local dependency between game objects, and to count objects. Additionally, to reduce the number of levels necessary to train the GAN, we propose a bootstrapping mechanism in which playable generated levels are added to the training set. The results demonstrate that the new approach does not only generate a larger number of levels that are playable but also generates fewer duplicate levels compared to a standard GAN. | en_GB |
dc.language.iso | mt | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Computer games -- Design | en_GB |
dc.subject | Computer bootstrapping | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.subject | Machine learning | en_GB |
dc.title | Bootstrapping conditional GANs for video game level generation | en_GB |
dc.type | conferenceObject | 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.bibliographicCitation.conferencename | 2020 IEEE Conference on Games (CoG) | en_GB |
dc.bibliographicCitation.conferenceplace | Osaka, Japan, 24-27/08/2020 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/CoG47356.2020.9231576 | - |
Appears in Collections: | Scholarly Works - InsDG |
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Bootstrapping_Conditional_GANs_for_Video_Game_Level_Generation_2020.pdf Restricted Access | 529.84 kB | Adobe PDF | View/Open Request a copy |
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