{"type":"ScholarlyArticle","title":"Inference without Chaining","authors":[{"name":"Alan M. Frisch","slug":"alan-m-frisch","given":"Alan M.","family":"Frisch","affiliation":""}],"abstract":"The problem of specifying, constructing , and understanding specialized, limited inference systems arises in many areas of AI. As a first step towards solving this problem this paper recommends the development of an inference engine that is limited by its inability to chain together two pieces of a representation in order to derive a third. A method for using model theory to specify limited inference is introduced and then used to specify an inference engine via a t hree-valued logic. This inference engine is proved to be the strongest one that does no chaining, modulo the way that it divides the representation into pieces. Thus , the specification captures the set of all inferences that require no chaining. This paper also surveys and compares a number of systems that do no chaining as well as some that allow only selected forms of chaining.","doi":null,"url":"https://ijcai.org/proceedings/1987/102","pdf":"https://ijcai.org/proceedings/1987/0102.pdf","markdown":"https://ijcai.org/proceedings/1987/102.md","bibtex":"https://ijcai.org/proceedings/1987/bibtex/102","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 Representation","track":"Knowledge Representation","number":102,"pages":{"first":515,"last":518},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}