{"type":"ScholarlyArticle","title":"Graphical Deep Knowledge for Intelligent Machine Drafting","authors":[{"name":"James Geller","slug":"james-geller","given":"James","family":"Geller","affiliation":""},{"name":"Stuart C. Shapiro","slug":"stuart-c-shapiro","given":"Stuart C.","family":"Shapiro","affiliation":""}],"abstract":"T he problem of Intelligent Machine Drafting is presented, a nd a de sc ription of an ex is ting implementation as part of a graphical generator functi on is given. The concept of Graphical Deep Knowledge is defined as a representational basis for Intelligent Machine Drafting problems as well as for physical object displays. A (partial) task domain analysis for Graphical Deep Knowledge is presented. Primitives that are necessary to deal with a world of 2-D forms and colors are introduced. Among them a re primitives for describing forms, positions, parts, attributes, sub-assemblies, and an abstraction hierarchy. The use of the \"linearity principle\" for knowledge structure derivation fr om natural language utterances is shown.","doi":null,"url":"https://ijcai.org/proceedings/1987/109","pdf":"https://ijcai.org/proceedings/1987/0109.pdf","markdown":"https://ijcai.org/proceedings/1987/109.md","bibtex":"https://ijcai.org/proceedings/1987/bibtex/109","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":109,"pages":{"first":545,"last":550},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}