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Implicit Fitness Functions for Evolving a Drawing Robot

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 4974)

Abstract

We describe an approach to artificially evolving a drawing robot using implicit fitness functions, which are designed to minimise any direct reference to the line patterns made by the robot. We employ this approach to reduce the constraints we place on the robot’s autonomy and increase its utility as a test bed for synthetically investigating creativity. We demonstrate the critical role of neural network architecture in the line patterns generated by the robot.

Keywords

  • Neuron Network
  • Food Particle
  • Motor Model
  • Line Pattern
  • Neural Network Architecture

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-540-78761-7_50
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References

  1. Bird, J., Stokes, D.: Minimal Creativity, Evaluation and Pattern Discrimination. In: Cardoso, A., Wiggins, G. (eds.) Proceedings of the 4th International Joint Conference on Computational Creativity, pp. 121–128 (2007)

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  2. Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press/Bradford Books, Cambridge (2000)

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  3. Cliff, D., Harvey, I., Husbands, P.: Explorations in Evolutionary Robotics. Adaptive Behavior 2, 73–110 (1993)

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  4. Perris, M.: Evolving Ecologically Inspired Drawing Behaviours. MSc dissertation, Department of Informatics, University of Sussex (2007)

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© 2008 Springer-Verlag Berlin Heidelberg

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Bird, J., Husbands, P., Perris, M., Bigge, B., Brown, P. (2008). Implicit Fitness Functions for Evolving a Drawing Robot. In: , et al. Applications of Evolutionary Computing. EvoWorkshops 2008. Lecture Notes in Computer Science, vol 4974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78761-7_50

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  • DOI: https://doi.org/10.1007/978-3-540-78761-7_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78760-0

  • Online ISBN: 978-3-540-78761-7

  • eBook Packages: Computer ScienceComputer Science (R0)