{"type":"ScholarlyArticle","title":"Solving Strong-Fault Diagnostic Models by Model Relaxation","authors":[{"name":"Alexander Feldman","slug":"alexander-feldman","given":"Alexander","family":"Feldman","affiliation":""},{"name":"Gregory M. Provan","slug":"gregory-m-provan","given":"Gregory M.","family":"Provan","affiliation":""},{"name":"Arjan J. C. van Gemund","slug":"arjan-j-c-van-gemund","given":"Arjan J. C.","family":"van Gemund","affiliation":""}],"abstract":"In Model-Based Diagnosis (MBD), the problem of computing a diagnosis in a strong-fault model (SFM) is computationally much harder than in a weak-fault model (WFM). For example, in propositional Horn models, computing the first minimal diagnosis in a weak-fault model (WFM) is in P but is NP-hard for strong-fault models. As a result, SFM problems of practical significance have not been studied in great depth within the MBD community. In this paper we describe an algorithm that renders the problem of computing a diagnosis in several important SFM subclasses no harder than a similar computation in a WFM. We propose an approach for efficiently computing minimal diagnoses for these subclasses of SFM that extends existing conflict-based algorithms like GDE (Sherlock) and CDA*. Experiments on ISCAS85 combinational circuits show (1) inference speedups with CDA* of up to a factor of 8, and (2) an average of 28% reduction in the average conflict size, at the price of an extra low-polynomial-time consistency check for a candidate diagnosis.Alexander Feldman, Gregory Provan, Arjan van Gemund","doi":null,"url":"https://ijcai.org/proceedings/2009/135","pdf":"https://ijcai.org/proceedings/2009/0135.pdf","markdown":"https://ijcai.org/proceedings/2009/135.md","bibtex":"https://ijcai.org/proceedings/2009/bibtex/135","volume":{"slug":"2009","title":"Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2009","url":"https://ijcai.org/proceedings/2009/","year":2009,"location":"Pasadena, California, USA","dates":"","editors":"Hiroaki Kitano","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":135,"pages":{"first":785,"last":789},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}