{"type":"ScholarlyArticle","title":"Symbolic Revision of Theories with M-of-N Rules","authors":[{"name":"Paul T. Baffes","slug":"paul-t-baffes","given":"Paul T.","family":"Baffes","affiliation":""},{"name":"Raymond J. Mooney","slug":"raymond-j-mooney","given":"Raymond J.","family":"Mooney","affiliation":""}],"abstract":"This paper presents a major rev1s1on of the EITHER propositional theory refinement system. Two issues are discussed. First, we show how run time efficiency can be greatly improved by changing from a exhaustive scheme for computing repairs to an iterative greedy method . Second, we show how to extend EITHER to refine M-of-N rules. The resulting algorithm, NEITHER (New EITHER), is more than an order of magnitude faster and produces significantly more accurate results with theories that fit the M-of-N format. To demonstrate the advantages of NEITHER, we present preliminary experimental results comparing it to EITHER and various other systems on refining the DNA promoter domain theory.","doi":null,"url":"https://ijcai.org/proceedings/1993/157","pdf":"https://ijcai.org/proceedings/1993/0157.pdf","markdown":"https://ijcai.org/proceedings/1993/157.md","bibtex":"https://ijcai.org/proceedings/1993/bibtex/157","volume":{"slug":"1993","title":"Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1993","url":"https://ijcai.org/proceedings/1993/","year":1993,"location":"Chambéry, France","dates":"","editors":"Ruzena Bajcsy","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Machine Learning","track":"Machine Learning","number":157,"pages":{"first":1135,"last":1141},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}