Habituation and Sensitisation Learning in ASMO Cognitive Architecture

  • Rony Novianto
  • Benjamin Johnston
  • Mary-Anne Williams
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8239)

Abstract

As social robots are designed to interact with humans in unstructured environments, they need to be aware of their surroundings, focus on significant events and ignore insignificant events in their environments. Humans have demonstrated a good example of adaptation to habituate and sensitise to significant and insignificant events respectively. Based on the inspiration of human habituation and sensitisation, we develop novel habituation and sensitisation mechanisms and include these mechanisms in ASMO cognitive architecture. The capability of these mechanisms is demonstrated in the ‘Smokey robot companion’ experiment. Results show that Smokey can be aware of their surroundings, focus on significant events and ignore insignificant events. ASMO’s habituation and sensitisation mechanisms can be used in robots to adapt to the environment. It can also be used to modify the interaction of components in a cognitive architecture in order to improve agents’ or robots’ performances.

Keywords

Habituation Sensitisation ASMO Cognitive Architecture 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Powell, R., Symbaluk, D., MacDonald, S., Honey, P.: Introduction to Learning and Behavior. Wadsworth Cengage Learning (2008)Google Scholar
  2. 2.
    Novianto, R., Johnston, B., Williams, M.A.: Attention in the asmo cognitive architecture. In: Proceedings of the First Annual Meeting of the BICA Society (2010)Google Scholar
  3. 3.
    Novianto, R., Williams, M.A.: Innate and learned emotion network. In: Samsonovich, A.V., Jóhannsdóttir, K.R. (eds.) Proceedings of the Second Annual Meeting of the BICA Society. Frontiers in Artificial Intelligence and Applications, vol. 233, pp. 263–268. IOS Press (2011)Google Scholar
  4. 4.
    Eisenstein, E., Eisenstein, D.: A behavioral homeostasis theory of habituation and sensitization: Ii. Further developments and predictions. Reviews in the Neurosciences 17(5), 533–558 (2006)CrossRefGoogle Scholar
  5. 5.
    Seel, N.: Encyclopedia of the Sciences of Learning. Springer (2011)Google Scholar
  6. 6.
    Sternberg, R.J., Mio, J., Mio, J.: Cognitive Psychology. Cengage Learning (2009)Google Scholar
  7. 7.
    Thompson, R., Spencer, W.: Habituation: a model phenomenon for the study of neuronal substrates of behavior. Psychological Review 73(1), 16 (1966)CrossRefGoogle Scholar
  8. 8.
    Rankin, C.H., Abrams, T., Barry, R.J., Bhatnagar, S., Clayton, D.F., Colombo, J., Coppola, G., Geyer, M.A., Glanzman, D.L., Marsland, S., et al.: Habituation revisited: an updated and revised description of the behavioral characteristics of habituation. Neurobiology of Learning and Memory 92(2), 135–138 (2009)CrossRefGoogle Scholar
  9. 9.
    Kohonen, T.: Self-organization and associative memory, 3rd edn. Springer-Verlag New York, Inc., New York (1989)CrossRefGoogle Scholar
  10. 10.
    Mareschal, D., French, R.M., Quinn, P.C., et al.: A connectionist account of asymmetric category learning in early infancy. Developmental Psychology 36(5), 635–645 (2000)CrossRefGoogle Scholar
  11. 11.
    Chang, C.: Improving hallway navigation in mobile robots with sensor habituation. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000, vol. 5, pp. 143–147. IEEE (2000)Google Scholar
  12. 12.
    Sirois, S.: Hebbian motor control in a robot-embedded model of habituation. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, IJCNN 2005, vol. 5, pp. 2772–2777. IEEE (2005)Google Scholar
  13. 13.
    Marsland, S., Nehmzow, U., Shapiro, J.: On-line novelty detection for autonomous mobile robots. Robotics and Autonomous Systems 51(23), 191–206 (2005)CrossRefGoogle Scholar
  14. 14.
    Cyr, A., Boukadoum, M.: Habituation: a non-associative learning rule design for spiking neurons and an autonomous mobile robots implementation. Bioinspiration & Biomimetics 8, 016007 (2013)Google Scholar
  15. 15.
    Sirois, S., Mareschal, D.: Models of habituation in infancy. Trends in Cognitive Sciences 6(7), 293–298 (2002)CrossRefGoogle Scholar
  16. 16.
    Stanley, J.C.: Computer simulation of a model of habituation. Nature 261, 146–148 (1976)CrossRefGoogle Scholar
  17. 17.
    Groves, P.M., Thompson, R.F.: Habituation: a dual-process theory. Psychological Review 77(5), 419 (1970)CrossRefGoogle Scholar
  18. 18.
    Damper, R.I., French, R.L., Scutt, T.W.: Arbib: an autonomous robot based on inspirations from biology. Robotics and Autonomous Systems 31(4), 247–274 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rony Novianto
    • 1
  • Benjamin Johnston
    • 1
  • Mary-Anne Williams
    • 1
  1. 1.Center of Quantum Computation and Intelligent SystemsUniversity of TechnologySydneyAustralia

Personalised recommendations