Biological Cybernetics

, Volume 42, Issue 1, pp 1–8

Landmark learning: An illustration of associative search

  • Andrew G. Barto
  • Richard S. Sutton
Article

Abstract

In a previous paper we defined the associative search problem and presented a system capable of solving it under certain conditions. In this paper we interpret a spatial learning problem as an associative search task and describe the behavior of an adaptive network capable of solving it. This example shows how naturally the associative search problem can arise and permits the search, association, and generalization properties of the adaptive network to bee clearly illustrated.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amari, S.: Neural theory of associaiton and concept-formation. Biol. Cybern.27, 175–185 (1977)Google Scholar
  2. Anderson, J.A., Silverstein, J.W., Ritz, S.A., Jones, R.S.: Distinctive features, categorical perception and probability learning; some applications of a neural model. Psychol. Rev.85, 413–451 (1977)Google Scholar
  3. Barto, A.G., Sutton, R.S., Brouwer, P.S.: Associative search network: a reinforcement learning associative memory. Biol. Cybern.40, 201–211 (1981)Google Scholar
  4. Fraenkel, G.S., Gunn, D.L.: The orientation of animals: kineses, taxes, and compass reactions. New York: Dover 1961Google Scholar
  5. Klopf, A.H.: Brain function and adaptive systems — a heterostatic theory. Air Force Cambridge Research Laboratories research report AFCRL-72-0164, Bedford, MA (1972) (AD742259). A summary in: Proceedings of the International Conference on Systems, Man and Cybernetics, IEEE Systems, Man and Cybernetics Society, Dallas, Texas, 1974Google Scholar
  6. Klopf, A.H.: Goal-seeking systems from goal-seeking components: implications for AI. The cognition and brain theory newsletter, Vol. III, No. 2, 54–62 (1979)Google Scholar
  7. Klopf, A.H.: The hedonistic neuron: a theory of memory, learning and intelligence. Washington, D.C.: Hemisphere (1980) (to be published)Google Scholar
  8. Kohonen, T.: Associative memory: a system theoretic approach. Berlin, Heidelberg, New York: Springer 1977Google Scholar
  9. Koshland, D.E., Jr.: A model regulatory system: bacterial chemotaxis. Physiol. Rev.59, 811–862 (1979)Google Scholar
  10. Kuipers, B.J.: Representing knowledge of large-scale space. M.I.T. Artificial Intelligence Laboratory report AI-TR-418. Cambridge, MA 1977Google Scholar
  11. Michie, D., Chambers, R.A.: BOXES: an experiment in adaptive control. In: Machine intelligence 2, pp. 137–152. Dale, E., Michie, D. (eds.) Edinburgh: Oliver and Boyd 1968Google Scholar
  12. Selfridge, O.G.: Tracking and trailing: adaptation in movement strategies. Unpublished draft (1978)Google Scholar

Copyright information

© Springer-Verlag 1981

Authors and Affiliations

  • Andrew G. Barto
    • 1
  • Richard S. Sutton
    • 1
  1. 1.Department of Computer and Information ScienceUniversity of MassachusettsAmherstUSA

Personalised recommendations