Skip to main content

Automatic Invention of Fitness Functions with Application to Scene Generation

  • Conference paper

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

Abstract

We investigate the automatic construction of visual scenes via a hybrid evolutionary/hill-climbing approach using a correlation-based fitness function. This forms part of The Painting Fool system, an automated artist which is able to render the scenes using simulated art materials. We further describe a novel method for inventing fitness functions using the HR descriptive machine learning system, and we combine this with The Painting Fool to generate and artistically render novel scenes. We demonstrate the potential of this approach with applications to cityscape and flower arrangement scene generation.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-540-78761-7_41
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   129.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-78761-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   169.00
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Boden, M.: The Creative Mind. Weidenfeld and Nicolson (1990)

    Google Scholar 

  2. Buchanan, B.: Creativity at the meta-level: Presidential address at AAAI-2000. AI Magazine 22(3), 13–28 (2001)

    MathSciNet  Google Scholar 

  3. Colton, S.: Refactorable numbers - a machine invention. Journal of Integer Sequences 2 (1999)

    Google Scholar 

  4. Colton, S.: Automated Theory Formation in Pure Mathematics. Springer, Heidelberg (2002)

    Google Scholar 

  5. Colton, S., Bundy, A., Walsh, T.: Automatic invention of integer sequences. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence (2000)

    Google Scholar 

  6. Colton, S., Muggleton, S.: Mathematical applications of Inductive Logic Programming. Machine Learning 64, 25–64 (2006)

    MATH  CrossRef  Google Scholar 

  7. Maher, M.L., Poon, J.: Co-evolution of the fitness function and design solution for design exploration. In: IEEE International Conference on Evolutionary Computation (1995)

    Google Scholar 

  8. McCorduck, P.: AARON’s Code: Meta-Art, Artificial Intelligence, and the Work of Harold Cohen. W.H. Freeman and Company, New York (1991)

    Google Scholar 

  9. Ritchie, G.: Assessing creativity. In: Wiggins, G. (ed.) Proceedings of the AISB 2001 Symposium on AI and Creativity in Arts and Science, pp. 3–11 (2001)

    Google Scholar 

  10. Sorge, V., Meier, A., McCasland, R., Colton, S.: Automatic construction and verification of isotopy invariants. In: Journal of Automated Reasoning (2007) (forthcoming)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Colton, S. (2008). Automatic Invention of Fitness Functions with Application to Scene Generation. 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_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-78761-7_41

  • 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)