NDLI: A model of symbol grounding in a temporal environment

Content Provider	IEEE Xplore Digital Library
Author	Bartell, B.T. Cottrell, G.W.
Copyright Year	1991
Description	Author affiliation: Dept. of Comput. & Eng., California Univ., La Jolla, CA, USA (Bartell, B.T.; Cottrell, G.W.)
Abstract	The authors present a simple recurrent neural network which was trained to generate sequences of symbols classifying sequences of perceptual input originating from an environment. The symbols generated, although from a small set, constitute classifications of an environment which is both analog valued and which has strongly temporal features. The symbol grounding problem is addressed by relating the learned categories directly to the perceptual input, and by analyzing the representation space constructed by the network to perform the task. The authors demonstrate that such a grounded system can exhibit useful generalization, and that the internal representation of the symbolic classes is usually different than the traditional predicate logic approach.
Starting Page	805
Ending Page	810
File Size	369180
Page Count	6
File Format	PDF
ISBN	0780301641
DOI	10.1109/IJCNN.1991.155282
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1991-07-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Grounding Motion pictures Recurrent neural networks Logic Computer science Character generation Performance analysis Neural networks Image sequences
Content Type	Text
Resource Type	Article

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