A Rule Chaining Architecture Using a Correlation Matrix Memory

  • James Austin
  • Stephen Hobson
  • Nathan Burles
  • Simon O’Keefe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7552)

Abstract

This paper describes an architecture based on superimposed distributed representations and distributed associative memories which is capable of performing rule chaining. The use of a distributed representation allows the system to utilise memory efficiently, and the use of superposition reduces the time complexity of a tree search to O(d), where d is the depth of the tree. Our experimental results show that the architecture is capable of rule chaining effectively, but that further investigation is needed to address capacity considerations.

Keywords

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kohonen, T.: Correlation Matrix Memories. IEEE Transactions on Computers, 353–359 (1972)Google Scholar
  2. 2.
    Baum, E.B., Moody, J., Wilczek, F.: Internal Representations for Associative Memory. Biol. Cybernetics 59, 217–228 (1988)MATHCrossRefGoogle Scholar
  3. 3.
    Austin, J.: Parallel Distributed Computation in Vision. In: IEE Colloquium on Neural Networks for Image Processing Applications, pp. 3/1–3/3 (1992)Google Scholar
  4. 4.
    Kustrin, D., Austin, J.: Connectionist Propositional Logic A Simple Correlation Matrix Memory Based Reasoning System. In: Wermter, S., Austin, J., Willshaw, D.J. (eds.) Emergent Neural Computational Architectures Based on Neuroscience. LNCS (LNAI), vol. 2036, pp. 534–546. Springer, Heidelberg (2001)CrossRefGoogle 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.
    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 2012

Authors and Affiliations

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

Personalised recommendations