Evolving the “Feeling” of Time Through Sensory-Motor Coordination: A Robot Based Model

  • Elio Tuci
  • Vito Trianni
  • Marco Dorigo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)

Abstract

In this paper, we aim to design decision-making mechanisms for an autonomous robot equipped with simple sensors, which integrates over time its perceptual experience in order to initiate a simple signalling response. Contrary to other similar studies, in this work the decision-making is uniquely controlled by the time-dependent structures of the agent’s controller, which in turn are tightly linked to the mechanisms for sensory-motor coordination. The results of this work show that a single dynamic neural network, shaped by evolution, makes an autonomous agent capable of “feeling” time through the flow of sensations determined by its actions.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Nonacs, P.: State dependent behavior and the marginal value theorem. Behavioral Ecology 12, 71–83 (2003)Google Scholar
  2. 2.
    Beer, R.D.: A dynamical systems perspective on agent-environment interaction. Artificial Intelligence 72, 173–215 (1995)CrossRefGoogle Scholar
  3. 3.
    Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press, Cambridge (2000)Google Scholar
  4. 4.
    Harvey, I., Husbands, P., Cliff, D.: Issues in evolutionary robotics. In: Meyer, J.A., Roitblat, H., Wilson, S. (eds.) Proc. of the 2nd Int. Conf. on Simulation of Adaptive Behavior, pp. 364–373. MIT Press, Cambridge (1992)Google Scholar
  5. 5.
    Beer, R.D., Gallagher, J.C.: Evolving dynamic neural networks for adaptive behavior. Adaptive Behavior 1, 91–122 (1992)CrossRefGoogle Scholar
  6. 6.
    Nolfi, S.: Evolving robots able to self-localize in the environment: The importance of viewing cognition as the result of processes occurring at different time scales. Connection Science 14, 231–244 (2002)CrossRefGoogle Scholar
  7. 7.
    Tuci, E., Harvey, I., Todd, P.M.: Using a net to catch a mate: Evolving CTRNNs for the dowry problem. In: Hallam, B., Floreano, D., Hallam, J., Hayes, G., Meyer, J.A. (eds.) Proceedings of SAB 2002, MIT press, Cambridge (2002)Google Scholar
  8. 8.
    Jakobi, N.: Evolutionary robotics and the radical envelope of noise hypothesis. Adaptive Behavior 6, 325–368 (1997)CrossRefGoogle Scholar
  9. 9.
    Nolfi, S.: EvoRob 1.1 User Manual. Institute of Psychology, National Research Council, CNR (2000), Available at http://gral.ip.rm.cnr.it/evorobot/simulator.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Elio Tuci
    • 1
  • Vito Trianni
    • 1
  • Marco Dorigo
    • 1
  1. 1.IRIDIAUniveristé Libre de BruxellesBruxellesBelgium

Personalised recommendations