Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
A topological search algorithm for ATPG
DAC '87 Proceedings of the 24th ACM/IEEE Design Automation Conference
Decomposable negation normal form
Journal of the ACM (JACM)
Unveiling the ISCAS-85 Benchmarks: A Case Study in Reverse Engineering
IEEE Design & Test
A signal correlation guided ATPG solver and its applications for solving difficult industrial cases
Proceedings of the 40th annual Design Automation Conference
Design diagnosis using Boolean satisfiability
Proceedings of the 2004 Asia and South Pacific Design Automation Conference
A Circuit SAT Solver With Signal Correlation Guided Learning
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
A two-step hierarchical algorithm for model-based diagnosis
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
On compiling system models for faster and more scalable diagnosis
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Efficient Genome Wide Tagging by Reduction to SAT
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Parametric abstraction of behavioral modes for model-based diagnosis
AI Communications
New compilation languages based on structured decomposability
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
FRACTAL: efficient fault isolation using active testing
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Solving strong-fault diagnostic models by model relaxation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A benchmark diagnostic model generation system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
A model-based active testing approach to sequential diagnosis
Journal of Artificial Intelligence Research
Sequential diagnosis by abstraction
Journal of Artificial Intelligence Research
An efficient diagnosis algorithm for inconsistent constraint sets
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Computing minimum-cardinality diagnoses by model relaxation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Due to large search spaces, diagnosis of combinational circuits is often practical for finding only single and double faults. In principle, system models can be compiled into a tractable representation (such as DNNF) on which faults of arbitrary cardinality can be found efficiently. For large circuits, however, compilation can become a bottleneck due to the large number of variables necessary to model the health of individual gates. We propose a novel method that greatly reduces this number, allowing the compilation, as well as the diagnosis, to scale to larger circuits. The basic idea is to identify regions of a circuit, called cones, that are dominated by single gates, and model the health of each cone with a single health variable. When a cone is found to be possibly faulty, we diagnose it by again identifying the cones inside it, and so on, until we reach a base case. We show that results combined from these hierarchical sessions are sound and complete with respect to minimum-cardinality diagnoses. We implement this method on top of the diagnoser developed by Huang and Darwiche in 2005, and present evidence that it significantly improves the efficiency and scalability of diagnosis on the ISCAS-85 circuits.