{"type":"ScholarlyArticle","title":"Automated Benchmark Model Generators for Model-Based Diagnostic Inference","authors":[{"name":"Gregory M. Provan","slug":"gregory-m-provan","given":"Gregory M.","family":"Provan","affiliation":""},{"name":"Jun Wang","slug":"jun-wang","given":"Jun","family":"Wang","affiliation":""}],"abstract":"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.","doi":null,"url":"https://ijcai.org/proceedings/2007/81","pdf":"https://ijcai.org/proceedings/2007/0081.pdf","markdown":"https://ijcai.org/proceedings/2007/81.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/81","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":81,"pages":{"first":513,"last":517},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}