Uncertainty in Artificial Intelligence
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A Temporal Bayesian Network for Diagnosis and Prediction
Gustavo Arroyo-Figueroa, Luis Sucar
Abstract:
Diagnosis and prediction in some domains, like medical and industrial diagnosis, require a representation that combines uncertainty management and temporal reasoning. Based on the fact that in many cases there are few state changes in the temporal range of interest, we propose a novel representation called Temporal Nodes Bayesian Networks (TNBN). In a TNBN each node represents an event or state change of a variable, and an arc corresponds to a causal-temporal relationship. The temporal intervals can differ in number and size for each temporal node, so this allows multiple granularity. Our approach is contrasted with a dynamic Bayesian network for a simple medical example. An empirical evaluation is presented for a more complex problem, a subsystem of a fossil power plant, in which this approach is used for fault diagnosis and prediction with good results.
Keywords: Bayesian networks, temporal, diagnosis, prediction
Pages: 13-20
PS Link:
PDF Link: /papers/99/p13-arroyo-figueroa.pdf
BibTex:
@INPROCEEDINGS{Arroyo-Figueroa99,
AUTHOR = "Gustavo Arroyo-Figueroa and Luis Sucar",
TITLE = "A Temporal Bayesian Network for Diagnosis and Prediction",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "13--20"
}


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