Connectionist symbol processing
Author(s)
Bibliographic Information
Connectionist symbol processing
（Special issues of Artificial intelligence, an international journal）（Bradford book）
MIT Press, 1991, c1990
1st MIT Press ed
- : pbk
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Note
"Reprinted from Artificial intelligence, an international journal, volume 46, numbers 1-2, 1990"--T.p. verso
Includes bibliographical references and index
Description and Table of Contents
Description
Addressing the current tension within the artificial intelligence community between advocates of powerful symbolic representations that lack efficient learning procedures and advocates of relatively simple learning procedures that lack the ability to represent complex structures effectively.
The six contributions in Connectionist Symbol Processing address the current tension within the artificial intelligence community between advocates of powerful symbolic representations that lack efficient learning procedures and advocates of relatively simple learning procedures that lack the ability to represent complex structures effectively. The authors seek to extend the representational power of connectionist networks without abandoning the automatic learning that makes these networks interesting.Aware of the huge gap that needs to be bridged, the authors intend their contributions to be viewed as exploratory steps in the direction of greater representational power for neural networks. If successful, this research could make it possible to combine robust general purpose learning procedures and inherent representations of artificial intelligence-a synthesis that could lead to new insights into both representation and learning.
Table of Contents
- BoltzCONS - dynamic symbol structures in a connectionist network, D.S. Touretzky
- mapping part-whole hierarchies into connectionist networks, G. E. Hinton
- recursive distributed representations, J.B. Pollack
- mundane reasoning by settling on a plausible model, M. Derthick
- tensor product variable binding and the representation of symbolic structures in connectionist systems, P. Smolensky
- learning and applying contextual constraints in sentence comprehension, M.F. St. John and J.L. McClelland.
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