Skip to main content

A Brain-Like Design to Learn Optimal Decision Strategies in Complex Environments

  • Chapter
Dealing with Complexity

Abstract

In the development of learning systems and neural networks, the issue of complexity occurs at many levels of analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. P.Werbos, “Learning in the brain: an engineering interpretation”, in Learning as Self-Organization, K.Pribram, Ed., Erlbaum, 1996.

    Google Scholar 

  2. P. Werbos, “Neurocontrollers”, in Encyclopedia of Electronics and Electrical Engineering, J.Webster, Ed., Wiley, forthcoming. (Draft version incorporated into patent.)

    Google Scholar 

  3. R. Howard Dynamic Programming and Markhov Processes, MIT Press, 1960.

    Google Scholar 

  4. P.Werbos, “The cytoskeleton: Why it may be crucial to human learning and to neurocontrol”, Nanobiology, Vol. 1, No. 1, 1992.

    Google Scholar 

  5. D.P.Bertsekas and J.N.Tsitsiklis, Neurodynamic Programming, Athena Scientific, Belmont Mass., 1996.

    Google Scholar 

  6. R.Sutton, “TD Models: Modeling the World at a Mixture ofTime Scales.” CMPSCI Technical Report 95–114. U.Mass. Amherst, December 1995, later published in Proc. 12th Int. Conf Machine Learning, 531–539, Morgan Kaufmann, 1995.

    Google Scholar 

  7. J.Albus, Outline of Intelligence, IEEE Trans. Systems, Man and Cybernetics, Vol. 21, No. 2, 1991.

    Article  Google Scholar 

  8. D.White and D. Sofge, eds, Handbook of Intelligent Control, Van Nostrand, 1992.

    Google Scholar 

  9. Vernon Brooks, The Neural Basis of Motor Control, Oxford U. Press, 198_.

    Google Scholar 

  10. H.Ritter, T.Martinetz, and K.Schulten, Neural Computation and Self-Organizing Maps, Addison-Wesley, 1992.

    Google Scholar 

  11. D.S.Levine and S.J.Leven, Motivation, Emotion, and Goal Direction in Neural Networks, Erlbaum, 1992.

    Google Scholar 

  12. P.Werbos & X.Z.Pang, “Generalized maze navigation: SRN critics solve what feedforward or Hebbian nets cannot”, Proc. Conf Systems, Man and Cybernetics (SMC) (Beijing), IEEE, 1996. (An earlier version appeared in WCNN96 Proc,Erlbaum, 1996.)

    Google Scholar 

  13. X.Z.Pang & P.Werbos, “Neural network design for J function approximation in dynamic programming”, Math. Modelling and Scientific Computing (a Principia Scientia journal), special issue on neural nets, winter 1996–1997

    Google Scholar 

  14. P.Werbos, “Supervised learning: can it escape its local minimum”,WCNN93Proceedings, Erlbaum, 1993. Reprinted in Theoretical Advances in Neural Computation and Learning, V. Roychowdhury et al, Eds., Kluwer, 1994

    Google Scholar 

  15. D.Levine and W. Elsberry, Eds., Optimality in Biological and Artificial Networks, Erlbaum, 1996.

    MATH  Google Scholar 

  16. P.Werbos, “Optimal neurocontrol: Practical benefits, new results and biological evidence”, Proc. World Cong. on Neural Networks(WCNN95), Erlbaum, 1995

    Google Scholar 

  17. P.Werbos, “Optimization methods for brain-like intelligent control”, Proc. IEEE Conf. CDC, IEEE, 1995.

    Google Scholar 

  18. P.Werbos, The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting, Wiley, 1994.

    Google Scholar 

  19. P.Werbos, “Values, goals and utility in an engineering-based theory of mammalian intelligence” in Brain and Values, K.Pribram ed., Erlbaum, 1997.

    Google Scholar 

  20. K.Pribram, Brain and Perception: Holonomy and Structure in Figural Processing,Erlbaum 1991.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag London Limited

About this chapter

Cite this chapter

Kárný, M., Warwick, K., Kůrková, V. (1998). A Brain-Like Design to Learn Optimal Decision Strategies in Complex Environments. In: Kárný, M., Warwick, K., Kůrková, V. (eds) Dealing with Complexity. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1523-6_18

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-1523-6_18

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-76160-0

  • Online ISBN: 978-1-4471-1523-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics