{"type":"ScholarlyArticle","title":"Learning Strategies by Reasoning about Rules","authors":[{"name":"D. Paul Benjamin","slug":"d-paul-benjamin","given":"D. Paul","family":"Benjamin","affiliation":""}],"abstract":"One of the major weaknesses of current automated reasoning systems is that they lack the ability to control inference in a sophisticated, context-directed fashion. General strategies such as the set-of-support strategy are useful, but have proven inadequate for many individual problems. A strategy component is needed that possesses knowledge about many particular domains and problems. Such a body of knowledge would require a prohibitive amount of time to construct by hand. This leads us to consider means of automatically acquiring control knowledge from example proofs. One particular means of learning is explanation-based learning. This paper analyzes the basis of explanations - finding weakest preconditions that enable a particular rule to fire - to derive a representation within which explanations can be extracted from examples, generalized and used to guide the actions or a problem-solving system.","doi":null,"url":"https://ijcai.org/proceedings/1987/51","pdf":"https://ijcai.org/proceedings/1987/0051.pdf","markdown":"https://ijcai.org/proceedings/1987/51.md","bibtex":"https://ijcai.org/proceedings/1987/bibtex/51","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":51,"pages":{"first":256,"last":258},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}