---
title: "Conflict-Based Diagnosis: Adding Uncertainty to Model-based Diagnosis"
authors:
  - "Ildikó Flesch"
  - "Peter J. F. Lucas"
  - "Theo P. van der Weide"
doi: ""
url: "https://ijcai.org/proceedings/2007/59"
pdf: "https://ijcai.org/proceedings/2007/0059.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "380--1"
track: ""
keywords:
---

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

Ildikó Flesch, Peter J. F. Lucas, Theo P. van der Weide

*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.