{"type":"ScholarlyArticle","title":"Model-Based Diagnosis using Causal Networks","authors":[{"name":"Adnan Darwiche","slug":"adnan-darwiche","given":"Adnan","family":"Darwiche","affiliation":""}],"abstract":"This paper rests on several contributions. First , we introduce the notion of a consequence, which is a boolean expression that characterizes consistency-based diagnoses. Second, we introduce a basic algorithm for computing consequences when the system description is structured using a causa l network. We show that if the ca usal network has no undirected cycles, then a consequence has a linear size and can be computed in linear time. Fin ally, we show that diagnoses characterized by a consequence and meeting some preference criterion can be extracted from the consequence in time linear in its size. A dual set of results is provided for abductive diagnosis.","doi":null,"url":"https://ijcai.org/proceedings/1995/28","pdf":"https://ijcai.org/proceedings/1995/0028.pdf","markdown":"https://ijcai.org/proceedings/1995/28.md","bibtex":"https://ijcai.org/proceedings/1995/bibtex/28","volume":{"slug":"1995","title":"Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1995","url":"https://ijcai.org/proceedings/1995/","year":1995,"location":"Montréal, Québec, Canada","dates":"","editors":"Chris S. Mellish","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Automated Reasoning","track":"Automated Reasoning","number":28,"pages":{"first":211,"last":218},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}