Artificial Intelligence
Using crude probability estimates to guide diagnosis
Artificial Intelligence
Hierarchical model-based diagnosis
International Journal of Man-Machine Studies
Efficient implementation of a BDD package
DAC '90 Proceedings of the 27th ACM/IEEE Design Automation Conference
Characterizing diagnoses and systems
Artificial Intelligence
Building problem solvers
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Unveiling the ISCAS-85 Benchmarks: A Case Study in Reverse Engineering
IEEE Design & Test
Conflict-directed A* and its role in model-based embedded systems
Discrete Applied Mathematics
Model-based diagnosis using structured system descriptions
Journal of Artificial Intelligence Research
Diagnosing tree-decomposable circuits
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Computing minimal diagnoses by greedy stochastic search
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Computing observation vectors for max-fault min-cardinality diagnoses
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Hierarchical diagnosis of multiple faults
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Approximate model-based diagnosis using greedy stochastic search
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Approximate model-based diagnosis using greedy stochastic search
Journal of Artificial Intelligence Research
A diagnostic reasoning approach to defect prediction
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Sequential diagnosis by abstraction
Journal of Artificial Intelligence Research
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For many large systems the computational complexity of complete model-based diagnosis is prohibitive. In this paper we investigate the speedup of the diagnosis process by exploiting the hierarchy/locality as is typically present in well-engineered systems. The approach comprises a compile-time and a run-time step. In the first step, a hierarchical CNF representation of the system is compiled to hierarchical DNF of adjustable hierarchical depth. In the second step, the diagnoses are computed from the hierarchical DNF and the actual observations. Our hierarchical algorithm, while sound and complete, allows large models to be diagnosed, where compiletime investment directly translates to run-time speedup. The benefits of our approach are illustrated by using weak-fault models of real-world systems, including the ISCAS-85 combinatorial circuits. Even for these non-optimally partitioned problems the speedup compared to traditional approaches ranges in the hundreds.