Title

Dance evolution : interactively evolving neural networks to control dancing three-dimensional models

Abstract

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.

Notes

This item is only available in print in the UCF Libraries. If this is your thesis or dissertation, you can help us make it available online for use by researchers around the world by downloading and filling out the Internet Distribution Consent Agreement. You may also contact the project coordinator Kerri Bottorff for more information.

Thesis Completion

2009

Semester

Spring

Advisor

Stanley, Kenneth O.

Degree

Bachelor of Science (B.S.)

College

College of Engineering and Computer Science

Degree Program

Computer Science

Subjects

Dissertations, Academic -- Engineering and Computer Science;Engineering and Computer Science -- Dissertations, Academic

Format

Print

Identifier

DP0022333

Language

English

Access Status

Open Access

Length of Campus-only Access

None

Document Type

Honors in the Major Thesis

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