Advertisement

The Autopoietic Nature of the “Inner World”

A Study with Evolved “Blind” Robots
  • Michela Ponticorvo
  • Domenico Parisi
  • Orazio Miglino
Conference paper
  • 1k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5499)

Abstract

In this paper we propose a model of anticipatory behavior in robots which lack any sort of external stimulation. It would seem that in order to foresee an event and produce an anticipatory action an organism should receive some input from the external environment as a basis to predict what comes next. We ask if, even in absence of external stimulation, the organism can derive this knowledge from an “inner” world which “resonates” with the external world and is built up by an autopoietic process.

We describe a number of computer simulations that show how the behavior of living organisms can reflect the particular characteristics of the environment in which they live and can be adaptive with respect to that environment even if the organism obtains extremely little information from the environment through its sensors, or no information at all. We use the Evorobot simulator to evolve a population of artificial organisms (software robots) with the ability to explore a square arena. Results indicate that sensor-less robots are able to accomplish this exploration task by exploiting three mechanisms: (1) they rely on the internal dynamics produced by recurrent connections; (2) they diversify their behavior by employing a larger number of micro-behaviors; (3) they self-generate an internal rhythm which is coupled to the external environment constraints. These mechanisms are all mediated by the robot’s actions.

Keywords

Sensory Input Prospective Memory External World External Stimulation Internal World 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brandimonte, M., Einstein, G.O., McDaniel, M.A.: Prospective memory: Theory and application. Erlbaum, Mahwah (1996)Google Scholar
  2. 2.
    Guillot, A., Meyer, J.A.: Computer simulations of adaptive behavior in animats. In: Proceedings Computer Animation 1994. IEEE Computer Society, Los Alamitos (1994)Google Scholar
  3. 3.
    Grush, R.: The emulation theory of representation: Motor control, imagery, and perception. Behavioral and Brain Sciences 27, 377–442 (2004)Google Scholar
  4. 4.
    Harvey, I., Husbands, P., Cliff, D., Thompson, A., Jakobi, N.: Evolutionary Robotics at Sussex. In: Proceedings of ISRAM 1996, International Symposium on Robotics and Manufacturing, Montpellier, France, May 27-30 (1996)Google Scholar
  5. 5.
    Hesslow, G.: Conscious thought as simulation of behaviour and perception. Trends in Cognitive Sciences 6(6), 242–247 (2002)CrossRefGoogle Scholar
  6. 6.
    Hesslow, G., Jirenhed, D.-A.: The Inner World of a Simple Robot. Journal of Consciousness Studies 14-7, 85-96(12) (2007)Google Scholar
  7. 7.
    Jirenhed, D.-A., Hesslow, G., Ziemke, T.: Exploring internal simulation of perception in a mobile robot. Lund Univ. Cogn. Stud. 86, 107–113 (2001)Google Scholar
  8. 8.
    Langton, C.: Artificial Life. In: Langton, C. (ed.) Artificial Life, pp. 1–47. Addison-Wesley, Reading (1989)Google Scholar
  9. 9.
    Lungarella, M., Sporns, O.: Mapping information flow in sensorimotor networks. PLoS Comput. Biol. 2(10), e144 (2006)CrossRefGoogle Scholar
  10. 10.
    Maturana, H.R., Varela, F.J.: Autopoiesis and Cognition. Reidel, Boston (1980)CrossRefGoogle Scholar
  11. 11.
    Maturana, H.R., Varela, F.J.: The tree of knowledge. In: The biological roots of human understanding. Shambhala, Boston (1992)Google Scholar
  12. 12.
    Nolfi, S.: Evorobot 1.1 User Manual. Rome: Institute of Psychology, CNR (2000)Google Scholar
  13. 13.
    Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press/Bradford Books, Cambridge (2000)Google Scholar
  14. 14.
    Parisi, D.: Internal Robotics. Connection Science 16(4), 325–338 (2004)CrossRefGoogle Scholar
  15. 15.
    Parisi, D., Cecconi, F., Nolfi, S.: ECONETS: neural networks that learn in an environment. Network 1, 149–168 (1990)CrossRefGoogle Scholar
  16. 16.
    Piaget, J.: Biology and Knowledge. University of Chicago Press, Chicago (1971)Google Scholar
  17. 17.
    Todd, P.M.: The animat approach to intelligent behavior. Computer 25(11), 78–81 (1992)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Todd, P., Wilson, S., Somayaji, A., Yanco, H.: The blind breeding the blind: Adaptive behavior without looking. In: Cliff, D., Husbands, P., Meyer, J.-A., Wilson, S.W. (eds.) From Animals to Animats:The Third International Conference on Simulation of Adaptive Behavior, pp. 228–237. MIT Press, Cambridge (1994)Google Scholar
  19. 19.
    von Foerster, H.: What is Memory that It May Have Hindsight and Foresight as well? In: Bogoch (ed.) The Future of the Brain Sciences, Proceedings of a Conference held at the New York Academy, pp. 19–64. Plenum Press, New York (1969)Google Scholar
  20. 20.
    Wundt, W.: Grundzuge der Physiologicischen Psychologie. Verlag von Engelmann, Leipzig (1874)Google Scholar
  21. 21.
    Watson, J.B.: Psychology as the behaviorist views it. Psychological Review 20, 158–177 (1913)CrossRefGoogle Scholar
  22. 22.
    Watson, J.B.: Behavior: An introduction to comparative psychology. Holt, New York (1914)CrossRefGoogle Scholar
  23. 23.
    Wilson, S.W.: The animat path to AI. In: Meyer, J.-A., Wilson, S.W. (eds.) From animal to animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior. MIT Press/Bradford Books, Cambridge (1991)Google Scholar
  24. 24.
    Ziemke, T.: Cybernetics and embodied cognition: on the construction of realities in organisms and robots. Kybernetes 34(1/2), 118–128 (2005)CrossRefGoogle Scholar
  25. 25.
    Ziemke, T., Jirenhed, D.-A., Hesslow, G.: Internal simulation of perception: a minimal neuro-robotic model. Neurocomputing 68, 85–104 (2005)CrossRefGoogle Scholar
  26. 26.
    Ziemke, T.: The Embodied Self - Theories, hunches and robot models. Journal of Consciousness Studies 14(7), 167–179 (2007)Google Scholar
  27. 27.
    Ziemke, T.: On the role of emotion in biological and robotic autonomy. BioSystems 91, 401–408 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Michela Ponticorvo
    • 1
  • Domenico Parisi
    • 2
  • Orazio Miglino
    • 1
    • 2
  1. 1.Natural and Artificial Cognition LaboratoryUniversity of Naples “Federico II”Italy
  2. 2.Laboratory of Autonomous Robotics and Artificial Life, Institute of Cognitive Sciences and TechnologiesNational Research CouncilRomeItaly

Personalised recommendations