Overview of Popular Benchmark Sets
IEEE Design & Test
The Knowledge Engineering Review
Systematic topology analysis and generation using degree correlations
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Model-based diagnosis using structured system descriptions
Journal of Artificial Intelligence Research
Automated benchmark model generators for model-based diagnostic inference
IJCAI'07 Proceedings of the 20th international joint 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
On classification and modeling issues in distributed model-based diagnosis
AI Communications - Intelligent Engineering Techniques for Knowledge Bases
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Automated generators for synthetic models and data can playa crucial role in designing new algorithms/model-frameworks, given the sparsity of benchmark models for empirical analysis and the cost of generating models by hand. We describe an automated generator for benchmark models that is based on using a compositional modeling framework and employs random-graph models for the system topology. We choose the system topology that best matches the topology of the real-world system using a domain-analysis algorithm. To show the range of models for which this approach is applicable, we demonstrate our model-generation process using two examples of model generation optimized for a specific domain: (1) model-based diagnosis for discrete Boolean circuits, and (2) E.coli TRN networks for simulating gene expression.