Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-1996

Abstract

This article introduces a neural network based cognitive architecture termed Concept Hierarchy Memory Model (CHMM) for conceptual knowledge representation and commonsense reasoning. CHMM is composed of two subnetworks: a Concept Formation Network (CFN), that acquires concepts based on their sensory representations; and a Concept Hierarchy Network (CHN), that encodes hierarchical relationships between concepts. Based on Adaptive Resonance Associative Map (ARAM), a supervised Adaptive Resonance Theory (ART) model, CHMM provides a systematic treatment for concept formation and organization of a concept hierarchy. Specifically, a concept can be learned by sampling activities across multiple sensory fields. By chunking relations between concepts as cognitive codes, a concept hierarchy can be learned/modified through experience. Also, fuzzy relations between concepts can now be represented in terms of the weights on the links connecting them. Using a unified inferencing mechanism based on code firing, CHMM performs an important class of commonsense reasoning, including concept recognition and property inheritance.

Discipline

Computer Engineering | Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

International Journal of Neural Systems

Volume

7

Issue

3

First Page

305

Last Page

319

ISSN

0129-0657

Identifier

10.1142/S0129065796000270

Publisher

World Scientific Publishing

Additional URL

https://doi.org/10.1142/S0129065796000270

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