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

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Hybrid Neural Systems (Hybrid Neural Systems 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1778))

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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.

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Author information

Authors and Affiliations

  1. Information Technology Laboratory, GE Corporate Research and Development, One Research Circle, Niskayuna, NY, 12309, USA

    Nam Seog Park

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  1. Nam Seog Park
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Editor information

Editors and Affiliations

  1. University of Sunderland, SR6 0DD, Sunderland, United Kingdom

    Stefan Wermter

  2. CECS Department, University of Missouri-Colombia , 201 Engineering Building West, MO 65211-2060, Columbia, USA

    Ron Sun

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© 2000 Springer-Verlag Berlin Heidelberg

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Park, N.S. (2000). Addressing Knowledge-Representation Issues in Connectionist Symbolic Rule Encoding for General Inference. In: Wermter, S., Sun, R. (eds) Hybrid Neural Systems. Hybrid Neural Systems 1998. Lecture Notes in Computer Science(), vol 1778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10719871_6

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  • DOI: https://doi.org/10.1007/10719871_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67305-7

  • Online ISBN: 978-3-540-46417-4

  • eBook Packages: Springer Book Archive

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