Uncertainty in Artificial Intelligence
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Model-Based Diagnosis with Qualitative Temporal Uncertainty
Wolfgang Nejdl, Johann Gamper
Abstract:
In this paper we describe a framework for model-based diagnosis of dynamic systems, which extends previous work in this field by using and expressing temporal uncertainty in the form of qualitative interval relations a la Allen. Based on a logical framework extended by qualitative and quantitative temporal constraints we show how to describe behavioral models (both consistency- and abductive-based), discuss how to use abstract observations and show how abstract temporal diagnoses are computed. This yields an expressive framework, which allows the representation of complex temporal behavior allowing us to represent temporal uncertainty. Due to its abstraction capabilities computation is made independent of the number of observations and time points in a temporal setting. An example of hepatitis diagnosis is used throughout the paper.
Keywords: Model-Based Diagnosis, Temporal Reasoning, Qualitative Temporal Uncertainty, Dynamic
Pages: 432-439
PS Link: ftp://ftp.informatik.rwth-aachen.de/pub/reports/others/uai94.ps.gz
PDF Link: /papers/94/p432-nejdl.pdf
BibTex:
@INPROCEEDINGS{Nejdl94,
AUTHOR = "Wolfgang Nejdl and Johann Gamper",
TITLE = "Model-Based Diagnosis with Qualitative Temporal Uncertainty",
BOOKTITLE = "Proceedings of the Tenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "432--439"
}


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