Uncertainty in Artificial Intelligence
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Conflict and Surprise: Heuristics for Model Revision
Kathryn Laskey
Abstract:
Any probabilistic model of a problem is based on assumptions which, if violated, invalidate the model. Users of probability based decision aids need to be alerted when cases arise that are not covered by the aid's model. Diagnosis of model failure is also necessary to control dynamic model construction and revision. This paper presents a set of decision theoretically motivated heuristics for diagnosing situations in which a model is likely to provide an inadequate representation of the process being modeled.
Keywords:
Pages: 197-204
PS Link:
PDF Link: /papers/91/p197-laskey.pdf
BibTex:
@INPROCEEDINGS{Laskey91,
AUTHOR = "Kathryn Laskey ",
TITLE = "Conflict and Surprise: Heuristics for Model Revision",
BOOKTITLE = "Proceedings of the Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-91)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1991",
PAGES = "197--204"
}


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