A theory of diagnosis from first principles
Artificial Intelligence
The computational complexity of abduction
Artificial Intelligence - Special issue on knowledge representation
Overview of Popular Benchmark Sets
IEEE Design & Test
Model-based diagnosis using structured system descriptions
Journal of Artificial Intelligence Research
Generating application-specific benchmark models for complex systems
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Computing observation vectors for max-fault min-cardinality diagnoses
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Incremental algorithms for approximate compilation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Automated model generation for complex systems
MIC '08 Proceedings of the 27th IASTED International Conference on Modelling, Identification and Control
A benchmark diagnostic model generation system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
Distributed tree decomposition with privacy
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
On classification and modeling issues in distributed model-based diagnosis
AI Communications - Intelligent Engineering Techniques for Knowledge Bases
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The task of model-based diagnosis is NP-complete, but it is not known whether it is computationally difficult for the "average" real-world system. There has been no systematic study of the complexity of diagnosing real-world problems, and few good benchmarks exist to test this. Real-world-graphs, a mathematical framework that has been proposed as a model for complex systems, have empirically been shown to capture several topological properties of real-world systems. We describe the adequacy with which a real-world-graph can characterise the complexity of model-based diagnostic inference on real-world systems. We empirically compare the inference complexity of diagnosing models automatically generated using the real-world-graph framework with comparable models from well-known ISCAS circuit benchmarks. We identify parameters necessary for the real-world-graph framework to generate benchmark diagnosis circuit models with realistic properties.