Uncertainty in Artificial Intelligence
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Exploiting Uncertain and Temporal Information in Correlation
John Bigham
A modelling language is described which is suitable for the correlation of information when the underlying functional model of the system is incomplete or uncertain and the temporal dependencies are imprecise. An efficient and incremental implementation is outlined which depends on cost functions satisfying certain criteria. Possibilistic logic and probability theory (as it is used in the applications targetted) satisfy these criteria.
Pages: 22-29
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PDF Link: /papers/97/p22-bigham.pdf
AUTHOR = "John Bigham ",
TITLE = "Exploiting Uncertain and Temporal Information in Correlation",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "22--29"

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