Graphical Deep Knowledge for Intelligent Machine Drafting
Proceedings of the Tenth International Joint Conference on Artificial Intelligence. Knowledge Representation. Pages 545-550.
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.
@inproceedings{ijcai1987p109,
title = {Graphical Deep Knowledge for Intelligent Machine Drafting},
author = {Geller, James and Shapiro, Stuart C.},
booktitle = {Proceedings of the Tenth International Joint Conference on
Artificial Intelligence, {IJCAI-87}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John McDermott},
pages = {545--550},
year = {1987},
note = {Knowledge Representation},
url = {https://ijcai.org/proceedings/1987/109},
}
TY - CPAPER TI - Graphical Deep Knowledge for Intelligent Machine Drafting AU - Geller, James AU - Shapiro, Stuart C. T2 - Proceedings of the Tenth International Joint Conference on Artificial Intelligence, IJCAI-87 PB - International Joint Conferences on Artificial Intelligence PY - 1987 SP - 545 EP - 550 UR - https://ijcai.org/proceedings/1987/109 ER -Copied
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