NDLI: A Recency Inference Engine for Connectionist Knowledge Bases

Content Provider	Springer Nature Link
Author	Ghalwash, Atef Z.
Copyright Year	1998
Abstract	Knowledge-based neural networks (KBNNs) can be used as expert system knowledge bases. This approach shifts the interests in using connectionist knowledge bases for inferencing in an interactive fashion and giving reasonable justifications for their conclusions. The primary goal of this article is to present a good inference and control mechanism for such knowledge bases. For this purpose, the article develops a stand alone inference engine that uses a connectionist knowledge base, seeks to reduce the amount of data requested in order to reach a conclusion, and explains how a particular conclusion was reached. The inference engine was evaluated on illustrative example applications. Results obtained demonstrate that in spite of its simplicity the presented technique is superior to other techniques over sparse input knowledge bases.
Starting Page	201
Ending Page	215
Page Count	15
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	9
Issue Number	3
e-ISSN	15737497
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	1998-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial Intelligence (incl. Robotics) Mechanical Engineering Manufacturing, Machines, Tools
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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A Recency Inference Engine for Connectionist Knowledge Bases