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Gordon Monro

Gordon Monro. Faculty of Art and Design, Monash University, Melbourne, Australia www.gommog.com. One sixth of the way through a PhD in Art (part-time). Previously: BSc (Hons), PhD — in mathematics MMus (Composition) — in generative music and multimedia.

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Gordon Monro

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  1. Gordon Monro Faculty of Art and Design, Monash University, Melbourne, Australia www.gommog.com • One sixth of the way through a PhD in Art (part-time). • Previously: • BSc (Hons), PhD — in mathematics • MMus (Composition) — in generative music and multimedia

  2. Why is this insect the colour it is? Photograph taken near Coober Pedy, central Australia

  3. Why doesn’t this insect care what sees it? European wasp (a pest in Australia). Image from Wikipedia

  4. Proposed PhD project: Camouflage and mimicry: a portfolio of artificial life art • A system with three components: • Environmental background — trees, rocks,... • “Insects” — evolvable pattern generator, with Darwinian evolution. Some “insects” are poisonous (at a cost). Question of truth in signalling. • “Birds” — equipped with perceptual mechanism for classification of what they “see” into several classes: • Background • Food • Poison • Possibility of learning, cultural transmission, or Lamarckian evolution.

  5. My interests and what I hope to learn here: • What is the current state of practice in artificial creativity? • Has there been much work done, especially from an artistic standpoint, on the sort of scheme I have outlined? Or should I do something else? • I am interested in the aesthetics of procedures – simple rules, complex results. • Why are we so fascinated with the natural world, and is this related in some way to the previous point: we sense a hidden order? • What detailed work has there been on Gell-Mann’s “effective complexity”, and how does it relate to perception? • I would like to hear more about the inability of the philosophy of aesthetics to cope with generative art, and what follows from this. • (Personal) How have other people negotiated the transition from the world of science to the world of art?

  6. Evolvable pattern generator for the “insects” • I am experimenting with one based on the NeuroEvolution • of Augmenting Topologies (NEAT) evolvable neural networks of Stanley and Miikkulainen (2002). I understand that the PicBreeder collaborative online image breeder uses a similar mechanism (Secretan et al 2008, Stanley 2007). • The advantages are that although the images are generated by networks close to formula trees, • there is a principled, and fast, method for calculating (dis)-similarity of two networks, and hence for grouping organisms into species; • there is a principled (also fast) method of mating two non-identical networks. • I am using the YCbCr colour space (luminance and two chrominance channels, similar to YUV). • I have not yet considered what role shape should play, and at this stage I am only thinking about 2D.

  7. Discrimination method for the “birds” At present this is completely vague. I am thinking of a two-level scheme where there is a small number of feature detectors at the lower level, and some idea like the “conceptual spaces” of Gärdenfors at a higher level, leading eventually to a discrete classification and a corresponding action on the part of the “bird”. The discriminators could possibly be evolvable along the lines of Belpaeme (1999). Currently I am not planning to make any visual representations of the “birds”.

  8. References Belpaeme, T., 1999. Evolution of visual feature detectors. In Proceedings of the First European Workshop on Evolutionary Computation in Image analysis and Signal Processing (EvoIASP99, Göteborg, Sweden). Secretan, J. et al., 2008. Picbreeder: Evolving pictures collaboratively online. In Proceedings of the Computer Human Interaction Conference (CHI 2008). New York: ACM. Stanley, K.O., 2007. Compositional pattern producing networks: A novel abstraction of development. Genetic Programming and Evolvable Machines, 8(2), 131-162. Stanley, K.O. & Miikkulainen, R., 2002. Evolving neural networks throughaugmenting topologies. Evolutionary Computation, 10(1), 99-127.

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