{"type":"ScholarlyArticle","title":"Improving Efficiency by Learning Intermediate Concepts","authors":[{"name":"James Wogulis","slug":"james-wogulis","given":"James","family":"Wogulis","affiliation":""},{"name":"Pat Langley","slug":"pat-langley","given":"Pat","family":"Langley","affiliation":""}],"abstract":"One goal of explanation-based learning is to transform knowledge into an operational form for efficient use. Typically, this involves rewriting concept descriptions in terms of the predicates used to describe examples. In this paper we present RINCON, a system that extends domain theories from examples with the goal of maximizing classification efficiency. RINCON's basic learning operator involves the introduction of new intermediate concepts into a domain theory , which can be viewed as the inverse of the operationalization process. We discuss the system's learning algorithm and its re lation to work on explanation-based learning, incremental concept formation, representation change, and pattern matching. We also present experimental evidence from two natural domains that indicates the addition of intermediate concepts can improve classification efficiency.","doi":null,"url":"https://ijcai.org/proceedings/1989/105","pdf":"https://ijcai.org/proceedings/1989/0105.pdf","markdown":"https://ijcai.org/proceedings/1989/105.md","bibtex":"https://ijcai.org/proceedings/1989/bibtex/105","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":105,"pages":{"first":657,"last":661},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}