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.
| ISBN: | 9780262581066 |
| Publication date: | 16th January 1992 |
| Author: | G E Hinton |
| Publisher: | The MIT Press |
| Format: | Paperback |
| Pagination: | 270 pages |
| Series: | A Bradford Book |
| Genres: |
Psychology: states of consciousness |
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.
Connectionist Symbol Processing features in the following genres: Psychology: states of consciousness
Connectionist Symbol Processing is available in Paperback
Connectionist Symbol Processing was written by G E Hinton and published by The MIT Press
Connectionist Symbol Processing has 270 pages
Yes it is part of A Bradford Book series