{"type":"ScholarlyArticle","title":"FRACTAL: Efficient Fault Isolation Using Active Testing","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":"Model-Based Diagnosis (MBD) approaches often yield a large number of diagnoses, severely limiting their practical utility. This paper presents a novel active testing approach based on MBD techniques, called FRACTAL (FRamework for ACtive Testing ALgorithms), which, given a system description, computes a sequence of control settings for reducing the number of diagnoses. The approach complements probing, sequential diagnosis, and ATPG, and applies to systems where additional tests are restricted to setting a subset of the existing system inputs while observing the existing outputs. This paper evaluates the optimality of FRACTAL, both theoretically and empirically. FRACTAL generates test vectors using a greedy, next-best strategy and a low-cost approximation of diagnostic information entropy. Further, the approximate sequence computed by FRACTAL's greedy approach is optimal over all poly-time approximation algorithms, a fact which we confirm empirically. Extensive experimentation with ISCAS85 combinational circuits shows that FRACTAL reduces the number of remaining diagnoses according to a steep geometric decay function, even when only a fraction of inputs are available for active testing.Alexander Feldman, Gregory Provan, Arjan van Gemund","doi":null,"url":"https://ijcai.org/proceedings/2009/134","pdf":"https://ijcai.org/proceedings/2009/0134.pdf","markdown":"https://ijcai.org/proceedings/2009/134.md","bibtex":"https://ijcai.org/proceedings/2009/bibtex/134","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":134,"pages":{"first":778,"last":783},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}