GRAPHICAL DEEP KNOWLEDGE FOR INTELLIGENT MACHINE DRAFTING

James Geller; Stuart C. Shapiro

GRAPHICAL DEEP KNOWLEDGE FOR INTELLIGENT MACHINE DRAFTING

James Geller, Stuart C. Shapiro

Research output: Contribution to journal › Conference article › peer-review

Abstract

The problem of Intelligent Machine Drafting 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 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 are primitives for describing forms, positions, parts, attributes, sub-assemblies, and an abstraction hierarchy. The use of the "linearity principle" for knowledge structure derivation from natural language utterances is shown.

Original language	English (US)
Pages (from-to)	545-551
Number of pages	7
Journal	IJCAI International Joint Conference on Artificial Intelligence
Volume	1
State	Published - 1987
Externally published	Yes
Event	10th International Joint Conference on Artificial Intelligence, IJCAI 1987 - Milan, Italy Duration: Aug 23 1987 → Aug 28 1987

All Science Journal Classification (ASJC) codes

Artificial Intelligence

Cite this

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title = "GRAPHICAL DEEP KNOWLEDGE FOR INTELLIGENT MACHINE DRAFTING",

abstract = "The problem of Intelligent Machine Drafting 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 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 are primitives for describing forms, positions, parts, attributes, sub-assemblies, and an abstraction hierarchy. The use of the {"}linearity principle{"} for knowledge structure derivation from natural language utterances is shown.",

author = "James Geller and Shapiro, {Stuart C.}",

note = "Publisher Copyright: {\textcopyright} 1987 International Joint Conferences on Artificial Intelligence. All rights reserved.; 10th International Joint Conference on Artificial Intelligence, IJCAI 1987 ; Conference date: 23-08-1987 Through 28-08-1987",

year = "1987",

language = "English (US)",

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pages = "545--551",

journal = "IJCAI International Joint Conference on Artificial Intelligence",

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AU - Geller, James

AU - Shapiro, Stuart C.

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Y1 - 1987

N2 - The problem of Intelligent Machine Drafting 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 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 are primitives for describing forms, positions, parts, attributes, sub-assemblies, and an abstraction hierarchy. The use of the "linearity principle" for knowledge structure derivation from natural language utterances is shown.

AB - The problem of Intelligent Machine Drafting 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 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 are primitives for describing forms, positions, parts, attributes, sub-assemblies, and an abstraction hierarchy. The use of the "linearity principle" for knowledge structure derivation from natural language utterances is shown.

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M3 - Conference article

AN - SCOPUS:84936117049

SN - 1045-0823

VL - 1

SP - 545

EP - 551

JO - IJCAI International Joint Conference on Artificial Intelligence

JF - IJCAI International Joint Conference on Artificial Intelligence

T2 - 10th International Joint Conference on Artificial Intelligence, IJCAI 1987

Y2 - 23 August 1987 through 28 August 1987

ER -

GRAPHICAL DEEP KNOWLEDGE FOR INTELLIGENT MACHINE DRAFTING

Abstract

All Science Journal Classification (ASJC) codes

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