Conference
Keiki : towards realistic danmaku generation via sequential GANs
| Τίτλος: | Keiki : towards realistic danmaku generation via sequential GANs |
|---|---|
| Συγγραφείς: | Wang, Ziqi, Liu, Jialin, Yannakakis, Georgios N., 3rd IEEE Conference on Games |
| Στοιχεία εκδότη: | Institute of Electrical and Electronics Engineers |
| Έτος έκδοσης: | 2021 |
| Συλλογή: | University of Malta: OAR@UM / L-Università ta' Malta |
| Θεματικοί όροι: | Machine learning, Level design (Computer science), Computational intelligence, Computer games -- Programming |
| Περιγραφή: | Search-based procedural content generation methods have recently been introduced for the autonomous creation of bullet hell games. Search-based methods, however, can hardly model patterns of danmakus—the bullet hell shooting entity— explicitly and the resulting levels often look non-realistic. In this paper, we present a novel bullet hell game platform named Keiki, which allows the representation of danmakus as a parametric sequence which, in turn, can model the sequential behaviours of danmakus. We employ three types of generative adversarial networks (GANs) and test Keiki across three metrics designed to quantify the quality of the generated danmakus. The time-series GAN and periodic spatial GAN show different yet competitive performance in terms of the evaluation metrics adopted, their deviation from human-designed danmakus, and the diversity of generated danmakus. The preliminary experimental studies presented here showcase that potential of time-series GANs for sequential content generation in games. ; peer-reviewed |
| Τύπος εγγράφου: | conference object |
| Γλώσσα: | English |
| Relation: | Wang, Z., Liu, J., & Yannakakis, G. N. (2021). Keiki : towards realistic danmaku generation via sequential GANs. 3rd IEEE Conference on Games, Copenhagen.; https://www.um.edu.mt/library/oar/handle/123456789/80811 |
| Διαθεσιμότητα: | https://www.um.edu.mt/library/oar/handle/123456789/80811 |
| Rights: | info:eu-repo/semantics/openAccess ; 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. |
| Αριθμός Καταχώρησης: | edsbas.C28CD0EB |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://www.um.edu.mt/library/oar/handle/123456789/80811# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: Keiki : towards realistic danmaku generation via sequential GANs – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Ziqi%22">Wang, Ziqi</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Jialin%22">Liu, Jialin</searchLink><br /><searchLink fieldCode="AR" term="%22Yannakakis%2C+Georgios+N%2E%22">Yannakakis, Georgios N.</searchLink><br /><searchLink fieldCode="AR" term="%223rd+IEEE+Conference+on+Games%22">3rd IEEE Conference on Games</searchLink> – Name: Publisher Label: Publisher Information Group: PubInfo Data: Institute of Electrical and Electronics Engineers – Name: DatePubCY Label: Publication Year Group: Date Data: 2021 – Name: Subset Label: Collection Group: HoldingsInfo Data: University of Malta: OAR@UM / L-Università ta' Malta – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Level+design+%28Computer+science%29%22">Level design (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+intelligence%22">Computational intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+games+--+Programming%22">Computer games -- Programming</searchLink> – Name: Abstract Label: Description Group: Ab Data: Search-based procedural content generation methods have recently been introduced for the autonomous creation of bullet hell games. Search-based methods, however, can hardly model patterns of danmakus—the bullet hell shooting entity— explicitly and the resulting levels often look non-realistic. In this paper, we present a novel bullet hell game platform named Keiki, which allows the representation of danmakus as a parametric sequence which, in turn, can model the sequential behaviours of danmakus. We employ three types of generative adversarial networks (GANs) and test Keiki across three metrics designed to quantify the quality of the generated danmakus. The time-series GAN and periodic spatial GAN show different yet competitive performance in terms of the evaluation metrics adopted, their deviation from human-designed danmakus, and the diversity of generated danmakus. The preliminary experimental studies presented here showcase that potential of time-series GANs for sequential content generation in games. ; peer-reviewed – Name: TypeDocument Label: Document Type Group: TypDoc Data: conference object – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: Wang, Z., Liu, J., & Yannakakis, G. N. (2021). Keiki : towards realistic danmaku generation via sequential GANs. 3rd IEEE Conference on Games, Copenhagen.; https://www.um.edu.mt/library/oar/handle/123456789/80811 – Name: URL Label: Availability Group: URL Data: https://www.um.edu.mt/library/oar/handle/123456789/80811 – Name: Copyright Label: Rights Group: Cpyrght Data: info:eu-repo/semantics/openAccess ; 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. – Name: AN Label: Accession Number Group: ID Data: edsbas.C28CD0EB |
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| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Level design (Computer science) Type: general – SubjectFull: Computational intelligence Type: general – SubjectFull: Computer games -- Programming Type: general Titles: – TitleFull: Keiki : towards realistic danmaku generation via sequential GANs Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Ziqi – PersonEntity: Name: NameFull: Liu, Jialin – PersonEntity: Name: NameFull: Yannakakis, Georgios N. – PersonEntity: Name: NameFull: 3rd IEEE Conference on Games IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2021 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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