A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The computational complexity of abduction
Artificial Intelligence - Special issue on knowledge representation
Artificial Intelligence - Special issue on knowledge representation
A spectrum of logical definitions of model-based diagnosis
Computational Intelligence
Controlling inequality reasoning in a TMS-based analog diagnosis system
Readings in model-based diagnosis
When oscillators stop oscillating
Readings in model-based diagnosis
What's in SD?: Towards a theory of modeling for diagnosis
Readings in model-based diagnosis
Characterizing diagnoses and systems
Artificial Intelligence
The complexity of logic-based abduction
Journal of the ACM (JACM)
From statistical knowledge bases to degrees of belief
Artificial Intelligence
Processing disjunctions in temporal constraint networks
Artificial Intelligence
Diagnosis of large active systems
Artificial Intelligence
How to Reason With Uncertainty Knowledge
IPMU '90 Proceedings of the 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems: Uncertainty in Knowledge Bases
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems
Models and methods for plan diagnosis
Autonomous Agents and Multi-Agent Systems
Diagnosis of Simple Temporal Networks
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Random worlds and maximum entropy
Journal of Artificial Intelligence Research
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Conflict-based diagnosis: adding uncertainty to model-based diagnosis
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
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Dynamic domain abstraction through meta-diagnosis
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Physical impossibility instead of fault models
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
ACP: reason maintenance and inference control for constraint propagation over intervals
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Focusing on probable diagnoses
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Communicative commitments: Model checking and complexity analysis
Knowledge-Based Systems
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Models used for Model-Based Diagnosis usually assume that observations, and predictions based on the system description are accurate. In some domains, however, this assumption is invalid. Observations may not be accurate or the behavior model of the system does not allow for accurate predictions. Therefore, the accuracy of predictions, which is a function of the accuracy of the observed system inputs and the behavior model of the system, may differ from the accuracy of the observed system outputs. This paper investigates the consequences of using inaccurate values. The paper will show that traditional notions of preferred diagnoses such as abductive diagnosis and minimum consistency-based diagnosis are no longer suited if the available data has different accuracies. A new notion of preferred diagnoses, called maximal-confirmation diagnoses, is introduced.