Improving the Associative Rule Chaining Architecture

  • Nathan Burles
  • Simon O’Keefe
  • James Austin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8131)

Abstract

This paper describes improvements to the rule chaining architecture presented in [1]. The architecture uses distributed associative memories to allow the system to utilise memory efficiently, and superimposed distributed representations in order to reduce the time complexity of a tree search to O(d), where d is the depth of the tree. This new work reduces the memory required by the architecture, and can also further reduce the time complexity.

Keywords

rule chaining correlation matrix memory associative memory distributed representation parallel distributed computation 

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References

  1. 1.
    Austin, J., Hobson, S., Burles, N., O’Keefe, S.: A Rule Chaining Architecture Using a Correlation Matrix Memory. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012, Part I. LNCS, vol. 7552, pp. 49–56. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Kohonen, T.: Correlation Matrix Memories. IEEE Transactions on Computers, 353–359 (1972)Google Scholar
  3. 3.
    Gorodnichy, D.O.: Associative Neural Networks as Means for Low-Resolution Video-Based Recognition. IJCNN 2005, 3093–3098 (2005)Google Scholar
  4. 4.
    Ju, Q., O’Keefe, S., Austin, J.: Binary Neural Network Based 3D Facial Feature Localization. IJCNN 2009, 1462–1469 (2009)Google Scholar
  5. 5.
    Willshaw, D.J., Buneman, O.P., Longuet-Higgins, H.C.: Non-holographic Associative Memory. Nature 222, 960–962 (1969)CrossRefGoogle Scholar
  6. 6.
    Ritter, H., Martinetz, T., Schulten, K., Barsky, D., Tesch, M., Kates, R.: Neural Computation and Self-Organizing Maps: An Introduction. Addison Wesley, Redwood City (1992)MATHGoogle Scholar
  7. 7.
    Austin, J.: Parallel Distributed Computation in Vision. IEE Colloquium on Neural Networks for Image Processing Applications, 3/1–3/3 (1992)Google Scholar
  8. 8.
    Baum, E.B., Moody, J., Wilczek, F.: Internal Representations for Associative Memory. Biol. Cybernetics 59, 217–228 (1988)CrossRefMATHGoogle Scholar
  9. 9.
    Palm, G.: On the Storage Capacity of Associative Memories. In: Neural Assemblies, an Alternative Approach to Artificial Intelligence, pp. 192–199. Springer, New York (1982)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nathan Burles
    • 1
  • Simon O’Keefe
    • 1
  • James Austin
    • 1
  1. 1.Advanced Computer Architectures Group, Department of Computer ScienceUniversity of YorkYorkUK

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