{"type":"ScholarlyArticle","title":"Hierarchical Diagnosis Guided by Observations","authors":[{"name":"Luca Chittaro","slug":"luca-chittaro","given":"Luca","family":"Chittaro","affiliation":""},{"name":"Roberto Ranon","slug":"roberto-ranon","given":"Roberto","family":"Ranon","affiliation":""}],"abstract":"We propose a technique to improve the performance of hierarchical model-based diagnosis, based on structural abstraction. Given a hierarchical representation and the set of currently available observations, the technique is able to dynamically derive a tailored hierarchical representation to diagnose the current situation. We implement our strategy as an extension to the well-known Mozetic’s approach [Mozetic, 1992], and illustrate the obtained performance improvements. Our approach is more efficient than Mozetic’s one when, due to abstraction, fewer observations are available at the coarsest hierarchical levels.","doi":null,"url":"https://ijcai.org/proceedings/2001/80","pdf":"https://ijcai.org/proceedings/2001/0080.pdf","markdown":"https://ijcai.org/proceedings/2001/80.md","bibtex":"https://ijcai.org/proceedings/2001/bibtex/80","volume":{"slug":"2001","title":"Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2001","url":"https://ijcai.org/proceedings/2001/","year":2001,"location":"Seattle, Washington, USA","dates":"","editors":"","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":80,"pages":{"first":573,"last":577},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}