Artificial Intelligence
Model-based reasoning: troubleshooting
Exploring artificial intelligence
Readings in model-based diagnosis
Readings in model-based diagnosis
Handbook of logic in artificial intelligence and logic programming (vol. 3)
IEEE Transactions on Knowledge and Data Engineering
Normality and faults in logic-based diagnosis
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
A theory of diagnosis for incomplete causal models
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Using Possibilistic Logic for Modeling Qualitative Decision: ATMS-based Algorithms
Fundamenta Informaticae
| Hi-index | 0.00 |
An approach to fault isolation that exploits vastly incomplete models is presented. It relies on separate descriptions of each component behavior, together with the links between them, which enables a focusing of the reasoning to the relevant part of the system. As normal observations do not need explanation, the behavior of the components is limited to anomaly propagation. Diagnostic solutions are disorders (fault modes or abnormal signatures) that are consistent with the observations, as well as abductive explanations. An ordinal representation of uncertainty based on possibility theory provides a simple exception-tolerant description of the component behaviors. We can for instance distinguish effects that are more or less certainly present (or absent) and effects that are more or less possibly present (or absent) when a given anomaly is present. A realistic example illustrates the benefits of this approach.