Graphical Deep Knowledge for Intelligent Machine Drafting

The problem of Intelligent Machine Draft ing is presented, and a description of an existing implementation as part of a graphical generator function is given. The concept of Graphical Deep Knowledge is defined as a representational basis for Intelligent Machine Draft ing problems as wel l as for physical object displays. A (partial) task domain analysis for Graphical Deep Knowledge is presented. Primitives that are necessary to deal w i t h a wor ld of 2-D forms and colors are introduced. Among them are primit ives for describing forms, positions, parts, attr i butes, sub-assemblies, and an abstraction hierarchy. The use of the " l inearity principle" for knowledge structure derivation from natural language utterances is shown.