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Generative AI, decision-making, and collaborative choreography: how LSTM networks mirror human creativity
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Orientador(es)
Resumo(s)
ABSTRACT - 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.
Descrição
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Palavras-chave
Generative AI Contemporary dance Creativity Decision-making Habit
Contexto Educativo
Citação
Sevivas, 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
Editora
Fakultät 02 / Köln International School of Design