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
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Normality and faults in logic-based diagnosis
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
A fault detection approach based on machine learning models
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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We propose a methodology to diagnose multiple faults in complex systems. The approach is based on the Independent Choice Logic (ICL) and comprises two phases. In phase 1 we generate the explanations of the observed symptoms and handle the combinatorial explosion with a heuristic. In phase 2 we observe process signals to detect abnormal behavior that can lead us to identify the real faulted components. A proposal is made to automate this task with Dynamic Bayesian Networks (DBNs) embedded in the ICL formalism. The overall scheme is intended to give a definite diagnosis. ICL is a framework, which comprises a theory and a development environment. We show that ICL can be scaled-up to real-world, industrial-strength problems by using it in diagnosing faults in an electrical power transmission network.