Automated Benchmark Model Generators for Model-Based Diagnostic Inference
Gregory Provan, Jun Wang
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 roperties 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.