Repository logo
 
Publication

Generative AI, decision-making, and collaborative choreography: how LSTM networks mirror human creativity

2024-11-22Journal article Open access
Simple item page
datacite.subject.fosHumanidades:Artes
datacite.subject.sdg04:Educação de Qualidade
dc.contributor.authorSevivas, Cláudia
dc.contributor.authorRijmer, Sylvia
dc.contributor.authorEvola, Vito
dc.contributor.editorGrund, Mathias
dc.contributor.editorScherffig, Lasse
dc.date.accessioned2025-03-14T11:56:49Z
dc.date.available2025-03-14T11:56:49Z
dc.date.issued2024-11-22
dc.descriptionO documento integral está disponível através do link que se encontra no campo Versão do Editor
dc.description.abstractABSTRACT - This study explores the potential of generative AI, specifically Long Short-Term Memory (LSTM) networks, to advance collaborative choreographic composition within the framework of the Body Logic (BL) Method—a choreographic approach grounded incognitive science designed to challenge inherited habits and practices in contemporary dance. Through five cognitive tasks that emphasize different movement types and their qualities, we investigate how LSTM networks recognize established movement patterns and innovate by combining them in novel ways, mirroring the processes of human creativity. Furthermore, we examine how LSTM-generated sequences, derived from learned data, convey expressive qualities through a variety of movements. The AIgenerated movements closely follow the original movement trajectory but exhibit minor deviations attributable to the LSTM model's inherent prediction uncertainty. These variations illustrate the model's capability to introduce fresh elements while maintaining learned patterns, akin to human creativity. This research contributes novel perspectives on how technology can enrich artistic expression and challenge habitual decision-making in dance.por
dc.identifier.citationSevivas, C., Rijmer, S., & Evola, V. (2022). Generative AI, decision-making, and collaborative choreography: How LSTM networks mirror human creativity. In rrrreflect. Journal of Integrated Design Research, 1,6. DOI: https://doi.org/10.57684/COS-1270
dc.identifier.doihttps://doi.org/10.57684/COS-1270
dc.identifier.urihttp://hdl.handle.net/10400.21/21680
dc.language.isoeng
dc.peerreviewedyes
dc.publisherFakultät 02 / Köln International School of Design
dc.relation.hasversionhttps://cos.bibl.th-koeln.de/solrsearch/index/search/searchtype/collection/id/16240
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectGenerative AI
dc.subjectContemporary dance
dc.subjectCreativity
dc.subjectDecision-making
dc.subjectHabit
dc.titleGenerative AI, decision-making, and collaborative choreography: how LSTM networks mirror human creativitypor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue6
oaire.citation.titlerrrreflect. Journal of Integrated Design Research
oaire.citation.volume1
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Sevivas, Rijmer & Evola (2024). Generative AI, Decision-Making, and Collaborative Choreography How LSTM Networks Mirror Human Crea.pdf
Size:
1.96 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
4.03 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ESD - Artigos