Conflict-Based Diagnosis: Adding Uncertainty to Model-Based Diagnosis
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.
Cite
Text
Flesch et al. "Conflict-Based Diagnosis: Adding Uncertainty to Model-Based Diagnosis." International Joint Conference on Artificial Intelligence, 2007.Markdown
[Flesch et al. "Conflict-Based Diagnosis: Adding Uncertainty to Model-Based Diagnosis." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/flesch2007ijcai-conflict/)BibTeX
@inproceedings{flesch2007ijcai-conflict,
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 = {International Joint Conference on Artificial Intelligence},
year = {2007},
pages = {380-},
url = {https://mlanthology.org/ijcai/2007/flesch2007ijcai-conflict/}
}