{"type":"ScholarlyArticle","title":"Guiding Constructive Induction for Incremental Learning from Examples","authors":[{"name":"Larry Watanabe","slug":"larry-watanabe","given":"Larry","family":"Watanabe","affiliation":""},{"name":"Renee Elio","slug":"renee-elio","given":"Renee","family":"Elio","affiliation":""}],"abstract":"LAIR is a system that incrementally learns conjunctive concept descriptions from positive and negative examples. These concept descriptions are used to create and extend a domain theory that 1s applied, by means of constructive induction, to later learning tasks. Important issues for constructive induction are when to do it and how to control it. LAIR demonstrates how constructive induction can be controlled by (1) reducing it to simpler operations, (2) constraining the simpler operations to preserve relative correctness, (3) doing deductive inference on an as-needed basis to meet specific information requirements of learning subtasks, and (4) constraining the search space by subtaskdependentconstraints.","doi":null,"url":"https://ijcai.org/proceedings/1987/60","pdf":"https://ijcai.org/proceedings/1987/0060.pdf","markdown":"https://ijcai.org/proceedings/1987/60.md","bibtex":"https://ijcai.org/proceedings/1987/bibtex/60","volume":{"slug":"1987","title":"Proceedings of the Tenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1987","url":"https://ijcai.org/proceedings/1987/","year":1987,"location":"Milan, Italy","dates":"","editors":"John McDermott","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Knowledge Acquisition","track":"Knowledge Acquisition","number":60,"pages":{"first":293,"last":295},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}