“XEPA – Autonomous Intelligent Light and Sound Sculptures That Improvise Group Performances” by Galanter

  • ©Philip Galanter

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    XEPA - Autonomous Intelligent Light and Sound Sculptures That Improvise Group Performances

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Abstract:


    XEPA anticipates a future where machines form their own societies. Going beyond mere generative art, machines will exhibit artistic creativity with the addition of artistic judgment via computational aesthetic evaluation. In such a future our notions of aesthetics will undergo a radical translation. The XEPA intelligent sculptures create animated light and sound sequences. Each sculpture “watches” the others and modifies its own aesthetic behavior to create a collaborative, improvisational performance. No coordination information or commands are used. Each XEPA independently evaluates the aesthetics of the other sculptures, infers a theme or mood being attempted, and then modifies its own aesthetics to better reinforce that theme. Each performance is unique and widely varied. XEPA is an ever-evolving artwork, intended as a platform for ongoing experiments in computational aesthetic evaluation.

References:


    1. Galanter, Philip, “What is Generative Art? Complexity Theory as a Context for Art Theory,” International Conference on Generative Art (Milan: Generative Design Lab, Milan Polytechnic University, 2003).

    2. Gell-Mann, Murray, and Seth Lloyd, “Information Measures, Effective Complexity, and Total Information,” Complexity, Vol. 2, No. 1, 44–52 (1996).

    3. Boden, M.A., The Creative Mind: Myths and Mechanisms, 2nd ed. (New York: Routledge, 2004), xiii.

    4. Galanter, Philip, “Computational Aesthetic Evaluation: Past and Future,” Computers and Creativity, ed. J. McCormack and M. D’Inverno (Berlin: Springer, 2012).

    5. Livio, Mario, The Golden Ratio: The Story of Phi, the World’s Most Astonishing Number, 1st trade paperback ed. (New York: Broadway Books, 2003), viii.

    6. Zipf, G.K., Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology (Cambridge, MA: Addison-Wesley Press, 1949), xi.

    7. Manaris, Bill, et al., “Developing Fitness Functions for Pleasant Music: Zipf’s Law and Interactive Evolution Systems,” Applications of Evolutionary Computing Proceedings Vol. 3449, 498–507 (2005).

    8. Machado, Penousal, and Amílcar Cardoso, “All the Truth About NevAr,” Applied Intelligence Vol. 16, No. 2, 101–118 (2002).

    9. Arnheim, Rudolf, Art and Visual Perception: A Psychology of the Creative Eye, new, expanded, and revised ed. (Berkeley: University of California Press, 1974), x.

    10. Berlyne, D.E., Aesthetics and Psychobiology (New York: Appleton-Century-Crofts, 1971), xiv.

    11. Galanter, Philip, “What is Generative Art? Complexity Theory as a Context for Art Theory,” International Conference on Generative Art (Milan: Generative Design Lab, Milan Polytechnic University, 2003).

    12. Martindale, Colin, “Aesthetics, Psychobiology, and Cognition,” The Foundations of Aesthetics, Art, and Art Education, ed. Frank Farley and Ronald Neperud (New York: Praeger Publishers, 1988), 7–42.

    13. Martindale, Colin, et al., “The Effect of Extraneous Stimulation on Aesthetic Preference,” Empirical Studies of the Arts Vol. 23, No. 2, 83–91 (2005).

    14. Saunders, Rob, and John S. Gero, “Curious Agents and Situated Design Evaluations,” AIEDAM: Artificial Intelligence for Engineering Design Analysis and Manufacturing Vol. 18, No. 2, 153–161 (2004).

    15. Martindale, Colin, The Clockwork Muse: The Predictability of Artistic Change (New York: Basic Books, 1990), xiv.


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