TY  - JOUR
U1  - Wissenschaftlicher Artikel
A1  - Sevivas, Cláudia
A1  - Rijmer, Sylvia
A1  - Evola, Vito
ED  - Grund, Matthias
ED  - Scherffig, Lasse
T1  - Generative AI, Decision-Making, and Collaborative Choreography: How LSTM Networks Mirror Human Creativity
N2  - 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 in cognitive 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 AI-generated 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.
T3  - rrrreflect. Journal of Integrated Design Research - Special Issue 1,6 
KW  - Künstliche Intelligenz
KW  - Ästhetik
KW  - Materialität
KW  - Generative AI
KW  - Contemporary Dance
KW  - Creativity
KW  - Decision-Making
KW  - Habit
Y1  - 2024
UN  - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-12701
U6  - https://doi.org/10.57684/COS-1270
DO  - https://doi.org/10.57684/COS-1270
VL  - Special Issue 1 (2024)
ER  -