A theory of diagnosis from first principles
Artificial Intelligence
Decomposable negation normal form
Journal of the ACM (JACM)
A differential approach to inference in Bayesian networks
Journal of the ACM (JACM)
Hierarchical diagnosis of multiple faults
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Variable and value ordering for MPE search
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Clone: solving weighted Max-SAT in a reduced search space
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
The route to success: a performance comparison of diagnosis algorithms
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We propose a new approach based on model relaxation to compute minimum-cardinality diagnoses of a (faulty) system: We obtain a relaxed model of the system by splitting nodes in the system and compile the abstraction of the relaxed model into DNNF. Abstraction is obtained by treating self-contained sub-systems called cones as single components. We then use a novel branch-and-bound search algorithm and compute the abstract minimum-cardinality diagnoses of the system, which are later refined hierarchically, in a careful manner, to get all minimum-cardinality diagnoses of the system. Experiments on ISCAS-85 benchmark circuits show that the new approach is faster than the previous state-of-the-art hierarchical approach, and scales to all circuits in the suite for the first time.