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Conflict-Based Diagnosis: Adding Uncertainty to Model-based Diagnosis

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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.

Ildikó Flesch, Peter J. F. Lucas, and Theo P. van der Weide. 2007. Conflict-Based Diagnosis: Adding Uncertainty to Model-based Diagnosis. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07. IJCAI, 380–-1. https://ijcai.org/proceedings/2007/59
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