Uncertainty in Artificial Intelligence
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Automated Generation of Connectionist Expert Systems for Problems Involving Noise and Redundancy
Stephen Gallant
Abstract:
When creating an expert system, the most difficult and expensive task is constructing a knowledge base. This is particularly true if the problem involves noisy data and redundant measurements. This paper shows how to modify the MACIE process for generating connectionist expert systems from training examples so that it can accommodate noisy and redundant data. The basic idea is to dynamically generate appropriate training examples by constructing both a 'deep' model and a noise model for the underlying problem. The use of winner-take-all groups of variables is also discussed. These techniques are illustrated with a small example that would be very difficult for standard expert system approaches.
Keywords:
Pages: 212-221
PS Link:
PDF Link: /papers/87/p212-gallant.pdf
BibTex:
@INPROCEEDINGS{Gallant87,
AUTHOR = "Stephen Gallant ",
TITLE = "Automated Generation of Connectionist Expert Systems for Problems Involving Noise and Redundancy",
BOOKTITLE = "Proceedings of the Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-87)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1987",
PAGES = "212--221"
}


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