Dance evolution : interactively evolving neural networks to control dancing three-dimensional models
The impulse shared by all humans to express ourselves through dance represents a unique opportunity to artificially capture human creative expression. 1hls ambition aligns with the aim of artificial intelligence (AI) to study and emulate those aspects of human intelligence that are not readily reproduced in existing computer algorithms. As a first step toward addressing this challenge, this thesis describes Dance Evolution, which focuses on movements that are tied to a specific beat of music. Furthermore, Dance Evolution harnesses the users own taste to ex pl ore the new and interesting dances, allowing ta novel form of self-expression mediated by the computer, following the trend started by music and rhythm games. By implementing an algorithm that identifies the most prominent sounds within a song, Dance Evolution in effect allows artificial neural networks (ANNs) to listen to any song and exploit its rhythmic structure. Interactive evolution provides a tool for users to search increasingly intricate movement sequences by breeding their ANN controllers, in the same way that a gardener might explore interesting plants by breeding hybrids. The underlying idea in Dance Evolution is thus to create a novel mapping between sound and movement that evokes the spirit of casually dancing to the beat of a song.
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Stanley, Kenneth O.
Bachelor of Science (B.S.)
College of Engineering and Computer Science
Dissertations, Academic -- Engineering and Computer Science;Engineering and Computer Science -- Dissertations, Academic
Length of Campus-only Access
Honors in the Major Thesis
Dubbin, Greg A., "Dance evolution : interactively evolving neural networks to control dancing three-dimensional models" (2009). HIM 1990-2015. 818.