Conflict-Based Diagnosis: Adding Uncertainty to Model-based Diagnosis
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 380--1.
Abstract
Consistency-based diagnosis concerns using a model of the structure and behaviour of a system in order to analyse whether or not the system is malfunctioning. A well-known limitation of consistency-based diagnosis is that it is unable to cope with uncertainty. Uncertainty reasoning is nowadays done using Bayesian networks. In this field, a conflict measure has been introduced to detect conflicts between a given probability distribution and associated data. In this paper, we use a probabilistic theory to represent logical diagnostic systems and show that in this theory we are able to determine consistent and inconsistent states as traditionally done in consistency-based diagnosis. Furthermore, we analyse how the conflict measure in this theory offers a way to favour particular diagnoses above others. This enables us to add uncertainty reasoning to consistency-based diagnosis in a seamless fashion.
@inproceedings{ijcai2007p59,
title = {Conflict-Based Diagnosis: Adding Uncertainty to Model-based Diagnosis},
author = {Flesch, Ildikó and Lucas, Peter J. F. and van der Weide, Theo P.},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {380---1},
year = {2007},
url = {https://ijcai.org/proceedings/2007/59},
}
TY - CPAPER TI - Conflict-Based Diagnosis: Adding Uncertainty to Model-based Diagnosis AU - Flesch, Ildikó AU - Lucas, Peter J. F. AU - van der Weide, Theo P. T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 380 EP - -1 UR - https://ijcai.org/proceedings/2007/59 ER -Copied
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