Artificial Intelligence
A topological search algorithm for ATPG
DAC '87 Proceedings of the 24th ACM/IEEE Design Automation Conference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Focusing on probable diagnosis
Readings in model-based diagnosis
One step lookahead is pretty good
Readings in model-based diagnosis
Building problem solvers
Decision-theoretic troubleshooting
Communications of the ACM
Decomposable negation normal form
Journal of the ACM (JACM)
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Unveiling the ISCAS-85 Benchmarks: A Case Study in Reverse Engineering
IEEE Design & Test
A differential approach to inference in Bayesian networks
Journal of the ACM (JACM)
Hierarchical model-based diagnosis based on structural abstraction
Artificial Intelligence
A two-step hierarchical algorithm for model-based diagnosis
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Conflict-based diagnosis: adding uncertainty to model-based diagnosis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Hierarchical diagnosis of multiple faults
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
FRACTAL: efficient fault isolation using active testing
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
Systematic vs. non-systematic algorithms for solving the MPE task
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Hi-index | 0.00 |
When a system behaves abnormally, sequential diagnosis takes a sequence of measurements of the system until the faults causing the abnormality are identified, and the goal is to reduce the diagnostic cost, defined here as the number of measurements. To propose measurement points, previous work employs a heuristic based on reducing the entropy over a computed set of diagnoses. This approach generally has good performance in terms of diagnostic cost, but can fail to diagnose large systems when the set of diagnoses is too large. Focusing on a smaller set of probable diagnoses scales the approach but generally leads to increased average diagnostic costs. In this paper, we propose a new diagnostic framework employing four new techniques, which scales to much larger systems with good performance in terms of diagnostic cost. First, we propose a new heuristic for measurement point selection that can be computed efficiently, without requiring the set of diagnoses, once the system is modeled as a Bayesian network and compiled into a logical form known as d-DNNF. Second, we extend hierarchical diagnosis, a technique based on system abstraction from our previous work, to handle probabilities so that it can be applied to sequential diagnosis to allow larger systems to be diagnosed. Third, for the largest systems where even hierarchical diagnosis fails, we propose a novel method that converts the system into one that has a smaller abstraction and whose diagnoses form a superset of those of the original system; the new system can then be diagnosed and the result mapped back to the original system. Finally, we propose a novel cost estimation function which can be used to choose an abstraction of the system that is more likely to provide optimal average cost. Experiments with ISCAS-85 benchmark circuits indicate that our approach scales to all circuits in the suite except one that has a flat structure not susceptible to useful abstraction.