Readings in nonmonotonic reasoning
XED: diagnosing devices with hierarchic structure and known component failure modes
Proceedings of the sixth conference on Artificial intelligence applications
Compositional modeling: finding the right model for the job
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Characterizing diagnoses and systems
Artificial Intelligence
Automated modelling of physical systems
Automated modelling of physical systems
Hierarchical model-based diagnosis based on structural abstraction
Artificial Intelligence
Reasoning about assumptions in graphs of models
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Modeling when connections are the problem
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Automatic abstraction in component-based diagnosis driven by system observability
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
No faults in structure?: how to diagnose hidden interactions
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Task-dependent qualitative domain abstraction
Artificial Intelligence - Special volume on reformulation
Maximal-confirmation diagnoses
Knowledge-Based Systems
| Hi-index | 0.01 |
One of the most powerful tools designers have at their disposal is abstraction. By abstracting from the detailed properties of a system, the complexity of the overall design task becomes manageable. Unfortunately, faults in a system need not obey the neat abstraction levels of the designer. This paper presents an approach for identifying the abstraction level which is as simple as possible yet sufficient to address the task at hand. The approach chooses the desired abstraction level through applying model-based diagnosis at the meta-level, i.e., to the abstraction assumptions themselves.