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A GP Artificial Ant for image processing: preliminary experiments with EASEA.

  • Enzo Bolis
  • Christian Zerbi
  • Pierre Collet
  • Jean Louchet
  • Evelyne Lutton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2038)

Abstract

This paper describes how animat-based “food foraging” techniques may be applied to the design of low-level image processing algorithms. First, we show how we implemented the food foraging application using the EASEA software package. We then use this technique to evolve an animat and learn how to move inside images and detect high-gradient lines with a minimum exploration time. The resulting animats do not use standard “scanning + filtering” techniques but develop other image exploration strategies close to contour tracking. Experimental results on grey level images are presented.

Keywords

Fitness Function Grey Level Image Contour Detection Left Turn CMOS Image Sensor 
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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References

  1. [1]
    D.H. Ballard and C.M. Brown, Computer Vision, Prentice Hall, 1982.Google Scholar
  2. [2]
    R.J.V. Bertin and W.A. van de Grind, The Influence of Light-Dark Adaptation and Lateral Inhibition on Phototaxic Foraging: A Hypothetical Animal Study, Pages 141–167, Adaptive Behavior, Volume 5,Number 2, Fall 1996.CrossRefGoogle Scholar
  3. [3]
    V. Cantoni, M. Ferretti, S. Levialdi, R. Negrini and S. Stefanelli, Progress in Image Analysis and Processing, World Scientific,1991.Google Scholar
  4. [4]
    D. Cliff and S. Bullock, Adding “Foveal Vision” to Wilson’s Animat, Pages 49–72, Adaptive Behavior, Volume 2,Number 1, Summer 1993.CrossRefGoogle Scholar
  5. [5]
    P. Collet, E. Lutton, M. Schoenauer, J. Louchet, Take it EASEA, Parallel Problem Solving from Nature VI, vol 1917, Springer pp 891–901, Paris, September 2000.CrossRefGoogle Scholar
  6. [6]
    R.J. Collins and D.R. Jefferson. Antfarm: Towards simulated evolution. In S. Rasmussen, J. Farmer, C. Langton and C. Taylor, editors, Artificial Life II, Reading, Massachusetts, Addison-Wesley, 1991.Google Scholar
  7. [7]
    F.L. Crabbe, Michael G. Dyer, Observation and Imitation: Goal Sequence Learning in Neurally Controlled Construction Animats: VI-MAXSON, SAB 2000, Paris.Google Scholar
  8. [8]
    EASEA (EAsy Specification of Evolutionary Algorithms) home page: http://www-rocq.inria.fr/EASEA/
  9. [9]
    EO (Evolutionary Objects) home page: http://www.geneura.ugr.es/~jmerelo/EO.html
  10. [10]
  11. [11]
    P. Gaussier. Autonomous Robots interacting with an unknown world, Special Issue on Animat Approach to Control, Robotics and Autonomous Systems, 16, 1995.Google Scholar
  12. [12]
    R.C. Gonzalez, R.E. Woods, Digital Image Processing, Wiley, 1992Google Scholar
  13. [13]
    J. Ivins, J. Porrill, Statistical Snakes: Active Region Models, British Machine Vision Conference, York, Sep. 1994.Google Scholar
  14. [14]
    R.C. Jain, R. Kasturi, B.G. Schunck, Machine Vision, McGraw-Hill, 1995.Google Scholar
  15. [15]
    D. Jefferson, R. Collins, C. Cooper, M. Dyer, M. Flower, R. Korf, C. Taylor, A. Wang, Evolution as a theme in artificial life: the Genesys/Tracker system, Artificial life II, vol. X, Santa Fe Institute Studies in the Sciences of Complexity, Addison-Wesley, Feb. 1992, 549–578.Google Scholar
  16. [16]
    M. Köppen, B. Nickolay, Design of image exploring agent using genetic programming, Proceedings of the 4th International Conference on Soft Computing, volume 2, pages 549–552, Fukuoka, Japan, 30 Sep–5 Oct 1996, World Scientific, Singapore.Google Scholar
  17. [17]
    M. Köppen, B. Nickolay, Design of Image Exploring Agent using Genetic Programming. Fuzzy Sets and Systems, Special Issue on Softcomputing, 103 (1999) 303–315.CrossRefGoogle Scholar
  18. [18]
    J.R. Koza, Genetic Programming, MIT Press 1992.Google Scholar
  19. [19]
    J.R. Koza, J. Roughgarden and J.P. Rice, Evolution of Food-Foraging Strategies for the Caribbean Anolis Lizard Using Genetic Programming, Pages 171–199, Adaptive Behavior, Volume 1,Number 2, Fall 1992.CrossRefGoogle Scholar
  20. [20]
    I. Kuscu, A genetic Constructive Induction Model. In P.J. Angeline, Z. Michalewicz, M. Schoenauer, Xin Yao, and A. Zalzala, editors, Proceedings of the Congress on Evolutionary Computation, volume 1, pages 212–217, Mayflower Hotel, Washington D.C., USA, 6–9 July 1999. IEEE Press.Google Scholar
  21. [21]
    W.B. Langdon, Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming !, Kluwer, 1998.Google Scholar
  22. [22]
    W.B. Langdon and R. Poli, Better Trained Ants for Genetic Programming, Technical Report CSRP-98-12, April 1998, http://www.cs.bham.ac.uk/wbl.
  23. [23]
    W.B. Langdon and R. Poli, Why Ants are Hard, Technical Report: CSRP-98-4, January 1998, http://www.cs.bham.ac.uk/wbl.
  24. [24]
    J. Lévy Vehel, introduction to the multifractal analysis of images, in Fractal image encoding and analysis, Yuval Fischer ed., Springer Verlag, 1996.Google Scholar
  25. [25]
    J.A. Meyer, A. Guillot, From SAB90 to SAB94: Four Years of Animat Research, Proceedings of Third International Conference on Simulation of Adaptive Behavior. Brighton, England, 1994.Google Scholar
  26. [26]
    R. Moller, D. Lambrinos, R. Pfeifer, T. Labhart, and R. Wehner, Modeling Ant Navigation with an Autonomous Agent, From Animals to Animats 5, Proc. of the 5th Int. Conf. on Simulation of Adaptive Behavior, August 17-21, 1998, Zurich, Switzerland, edited by R. Pfeifer, B. Blumberg, J.-A. Meyer and S.W. Wilson.Google Scholar
  27. [27]
    T.J. Prescott, Spatial Representation for Navigation in Animats, Adaptive Behavior, Volume 4,Number 2, Fall 1995, 85–123.CrossRefGoogle Scholar
  28. [28]
    S.W. Wilson, Classifier systems and the animat problem, Machine Learning 2 (1987), 199–228.Google Scholar
  29. [29]
    S.W. Wilson, (1991). The animat approach to AI. In J. Meyer & S.W. Wilson (Eds), From Animals to Animats, Proceedings of the first International Conference on Simulation of Adaptive Behavior, Cambridge, MA: MIT Press, 15–21.Google Scholar
  30. [30]
    M. Witkowski, The Role of Behavioral Extinction in Animat Action Selection, SAB 2000, Paris, 2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Enzo Bolis
    • 1
  • Christian Zerbi
    • 1
  • Pierre Collet
    • 2
  • Jean Louchet
    • 1
  • Evelyne Lutton
    • 3
  1. 1.ENSTAParis cedex 15
  2. 2.Ecole PolytechniquePalaiseau cedex
  3. 3.INRIALe Chesnay cedex

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