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Authors
Advisor(s)
Abstract(s)
This paper describes a self-regulating artificial
ecosystem in continuous exposure to human observers.
Particles of variable morphology engage in local
interaction and give rise to emergent overall
audiovisual complexity. People only exercise influence
over autonomous behavior developing in the artificial
world. A machine-learning algorithm basically aims to
maximize audiovisual diversity by tracking changes in
systems behavior in relation to behavior in the artificial
world. We suggest rewarding human-machine
interaction to exist in the elaboration of dynamic
relationships between spatial and cognitive human
behavior and audiovisual performance in an artificial
universe.
Description
Keywords
Aesthetic human-machine interaction Machine learning Particle systems Autonomy Influence Emergence