Explicit and persistent knowledge in engineering drawing analysis

Update Item Information
Publication Type technical report
School or College College of Engineering
Department Computing, School of
Program Advanced Research Projects Agency
Creator Henderson, Thomas C.
Title Explicit and persistent knowledge in engineering drawing analysis
Date 2003-10-10
Description Domain knowledge permeates all aspects of the engineering drawing analysis process, including understanding the physical processes operating on the medium (i.e., paper), the image analysis techniques, and the interpretation semantics of the structural layout and contents of the drawing. Additionally, an understanding of the broader reverse engineering context, within which the drawing analysis takes place, should be exploited. Thus as part of a wider project on the reverse engineering of legacy systems, we have developed an agent-based engineering analysis system called NDAS (nonDeterministic Agent System). In this paper, we discuss the nature of such a system and how knowledge can be made explicit (both for agents and humans) and how performance models can be de?ned, calibrated, monitored, and improved over time through the use of persistent knowledge. A framework is proposed that allows computational agents to: (1) explore the threshold space for an optimal analysis of the drawing, (2) control information gain through agent invocation, (3) incorporate and communicate knowledge, and (4) inform the software engineering and system development with deep knowledge of the relationships between modules and their parameters (at least in a statistical sense).
Type Text
Publisher University of Utah
Subject Engineering drawing analysis
Subject LCSH Engineering drawings
Language eng
Bibliographic Citation Henderson, Thomas C. (2003). Explicit and persistent knowledge in engineering drawing analysis. UUCS-03-018.
Series University of Utah Computer Science Technical Report
Relation is Part of ARPANET
Rights Management ©University of Utah
Format Medium application/pdf
Format Extent 488,712 bytes
Source University of Utah School of Computing
ARK ark:/87278/s6rf6c7s
Setname ir_uspace
ID 703780
Reference URL https://collections.lib.utah.edu/ark:/87278/s6rf6c7s