{"type":"ScholarlyArticle","title":"Detecting and Correcting Errors of Omission After Explanation-Based Learning","authors":[{"name":"Michael J. Pazzani","slug":"michael-j-pazzani","given":"Michael J.","family":"Pazzani","affiliation":""}],"abstract":"In this paper, we address an issue that arises when the background knowledge used by explanationbased learning is incorrect. In particular, we consider the problems that can be caused by a domain theory that may be overly specific. Under this condition, generalizations formed by explanation-based learning will make errors of omission when they are relied upon to make predictions or explanations. We describe a technique for detecting errors of omission, assigning blame for the error of omission to an inference rule in the domain theory, and revising the domain theory to accommodate new examples.","doi":null,"url":"https://ijcai.org/proceedings/1989/114","pdf":"https://ijcai.org/proceedings/1989/0114.pdf","markdown":"https://ijcai.org/proceedings/1989/114.md","bibtex":"https://ijcai.org/proceedings/1989/bibtex/114","volume":{"slug":"1989","title":"Proceedings of the Eleventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1989","url":"https://ijcai.org/proceedings/1989/","year":1989,"location":"Detroit, Michigan, USA","dates":"August 20-25 1989","editors":"N. S. Sridharan","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Machine Learning","track":"Machine Learning","number":114,"pages":{"first":713,"last":717},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}