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Connectionist inference models.

A Browne1, R Sun

  • 1School of Computing, Information Systems and Mathematics, London Guildhall University, UK. abrowne@lgu.ac.uk

Neural Networks : the Official Journal of the International Neural Network Society
|January 5, 2002
PubMed
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Connectionist systems struggle with symbolic inference tasks like variable binding and rule-based reasoning. This survey reviews their performance, comparing them to symbolic systems for cognitive modeling applications.

Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Neuroscience

Background:

  • Symbolic inference tasks present a long-standing challenge for connectionist models.
  • Understanding how connectionist systems handle variable binding and rule-based reasoning is crucial.

Purpose of the Study:

  • To provide an extended survey of connectionist inference systems.
  • To analyze their performance in symbolic reasoning tasks.
  • To compare connectionist and symbolic approaches in cognitive modeling.

Main Methods:

  • Review of existing connectionist inference systems.
  • Analysis of representations (distributed vs. localist).
  • Evaluation of variable binding and rule-based reasoning capabilities.

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Main Results:

  • Different representations and systems offer distinct benefits and drawbacks.
  • Connectionist systems show limitations in complex symbolic reasoning.
  • Performance varies based on system architecture and representation.

Conclusions:

  • Connectionist systems face significant challenges in replicating symbolic inference.
  • The choice of representation impacts performance.
  • Further research is needed to bridge the gap between connectionist and symbolic AI for cognitive modeling.