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Addressing Knowledge-Representation Issues in Connectionist Symbolic Rule Encoding for General Inference

  • Nam Seog Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1778)

Abstract

This chapter describes one method for addressing knowledge representation issues that arise when a connectionist system replicates a standard symbolic style of inference for general inference. Symbolic rules are encoded into the networks, called structured predicate networks (SPN) using neuron-like elements. Knowledge-representation issues such as unification and consistency checking between two groups of unifying arguments arise when a chain of inference is formed over the networks encoding special type of symbol rules. These issues are addressed by connectionist sub-mechanisms embedded into the networks. As a result, the proposed SPN architecture is able to translate a significant subset of first-order Horn Clause expressions into a connectionist representation that may be executed very efficiently.

Keywords

Consistency Check Binding Interaction Oscillation Cycle Variable Binding Variable Argument 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Nam Seog Park
    • 1
  1. 1.Information Technology LaboratoryGE Corporate Research and DevelopmentNiskayunaUSA

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