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A Rule Chaining Architecture Using a Correlation Matrix Memory

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Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2012
DatePublished - 2012
Pages49-56
PublisherSPRINGER-VERLAG BERLIN
Volume7552
EditionPART 1
Original languageEnglish
ISBN (Electronic)978-3-642-33269-2
ISBN (Print)978-3-642-33268-5

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume7552
ISSN (Print)0302-9743

Abstract

This paper describes an architecture based on superimposed distributed representations and distributed associative memories which is capable of performing rule chaining. The use of a distributed representation allows the system to utilise memory efficiently, and the use of superposition reduces the time complexity of a tree search to O(d), where d is the depth of the tree. Our experimental results show that the architecture is capable of rule chaining effectively, but that further investigation is needed to address capacity considerations.

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