{"type":"ScholarlyArticle","title":"An Algorithm that Infers Theories from Facts","authors":[{"name":"Ehud Shapiro","slug":"ehud-shapiro","given":"Ehud","family":"Shapiro","affiliation":""}],"abstract":"A framework for inductive inference in logic is presented: a Mode/Inference Problem is defined, and it is shown that problems of machine learning and program synthesis from examples can be formulated naturally as model inference problems. A general. incremental inductive inference algorithm for solving model inference problems is developed. This algorithm is based on Popper's methodology of conjectures and refutations [II]. The algorithm can be shown to identify in the limit [3] any model in a family of complexity classes of models. is most powerful of its kind, and is flexible enough to have been successfully implemented for several concrete domains . The Model Inference System is a Prolog implementation of this algorithm, specialized to infer theories in Horn form . It can infer axiomatizations of concrete models from a small number of facts in a practical amount of time.","doi":null,"url":"https://ijcai.org/proceedings/1981/87","pdf":"https://ijcai.org/proceedings/1981/0087.pdf","markdown":"https://ijcai.org/proceedings/1981/87.md","bibtex":"https://ijcai.org/proceedings/1981/bibtex/87","volume":{"slug":"1981","title":"Proceedings of the Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1981","url":"https://ijcai.org/proceedings/1981/","year":1981,"location":"Vancouver, British Columbia, Canada","dates":"24-28 August 1981","editors":"Roger C. Schank","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Theoretical Foundations","track":"Theoretical Foundations","number":87,"pages":{"first":446,"last":450},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}