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A Structured Connectionist Approach to Inferencing and Retrieval

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Computational Architectures Integrating Neural And Symbolic Processes

Part of the book series: The Springer International Series In Engineering and Computer Science ((SECS,volume 292))

Abstract

Simple connectionist models have generally been unable to perform natural language understanding or memory retrieval beyond simple stereotypical situations that they have seen before. This is because they have had difficulties representing and applying general knowledge rules that specifically require variables, barring them from performing the high-level inferencing necessary for planning, reasoning, and natural language understanding. This chapter describes ROBIN, a structured (i.e., localist) connectionist model capable of massively-parallel high-level inferencing requiring variable bindings and rule application, and REMIND, a model based on ROBIN that explores the integration of language understanding and episodic memory retrieval in a single spreading-activation mechanism.

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Author information

Authors and Affiliations

  1. Artificial Intelligence Laboratory Computer Science Department, University of California, Los Angeles, California, 90024

    Trent E. Lange

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  1. Trent E. Lange
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Editor information

Editors and Affiliations

  1. The University of Alabama, Tuscaloosa, AL, USA

    Ron Sun

  2. Sun Microsystems Laboratories, Chelmsford, MA, USA

    Lawrence A. Bookman

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© 1995 Kluwer Academic Publishers

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Lange, T.E. (1995). A Structured Connectionist Approach to Inferencing and Retrieval. In: Sun, R., Bookman, L.A. (eds) Computational Architectures Integrating Neural And Symbolic Processes. The Springer International Series In Engineering and Computer Science, vol 292. Springer, Boston, MA. https://doi.org/10.1007/978-0-585-29599-2_3

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  • DOI: https://doi.org/10.1007/978-0-585-29599-2_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-7923-9517-1

  • Online ISBN: 978-0-585-29599-2

  • eBook Packages: Springer Book Archive

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