{"type":"ScholarlyArticle","title":"Abstraction in Problem Solving and Learning","authors":[{"name":"Amy Unruh","slug":"amy-unruh","given":"Amy","family":"Unruh","affiliation":""},{"name":"Paul S. Rosenbloom","slug":"paul-s-rosenbloom","given":"Paul S.","family":"Rosenbloom","affiliation":""}],"abstract":"Abstraction has proven to be a powerful tool for controlling the combinatorics of a problemso lving search. It is also of critical importance for learning systems. In this article we present, and evaluate experimentally, a general abstraction method - impasse-driven abstractionwhich is able to provide necessary assistance to both problem solving and learning. It reduces the amount of time required to solve problems, and the time required to learn new rules . In add ition, it results in the acquisition of rules that are more general than would have otherwise been learned .","doi":null,"url":"https://ijcai.org/proceedings/1989/109","pdf":"https://ijcai.org/proceedings/1989/0109.pdf","markdown":"https://ijcai.org/proceedings/1989/109.md","bibtex":"https://ijcai.org/proceedings/1989/bibtex/109","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":109,"pages":{"first":681,"last":686},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}