Symbolic knowledge encoding using a dynamic binding mechanism and an embedded inference mechanism

N.S. Park, D. Robertson, K. Stenning

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter describes how synchronous activity between neuron elements can be used to build a dynamic binding mechanism and knowledge encoding mechanisms in a connectionist manner. The purpose of these mechanisms is to build a connectionist inference architecture that can replicate common symbolic styles of inference. To build such an inference architecture, an extended temporal synchrony approach (Park, Robertson, & Stenning, 1995) is used as a basic building block. This is a revision and extension of an approach to the dynamic binding problem in the connectionist systems, proposed by Shastri and Ajjanagadde (1993). In addition, we introduce a set of algorithms that gives us a means of compiling a class of symbolic rules into a uniform inference network called a structured predicate network. This is used as a connectionist knowledge encoding mechanism which encodes symbolic rules and supports very fast inference.
Original languageEnglish
Title of host publicationOscillations in Neural Systems
EditorsD. Levine, B. Brown, T. Shirey
PublisherLawrence Erlbaum Associates
Pages343-368
Number of pages26
ISBN (Print)0-8058-2066-3
Publication statusPublished - 1999

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