A Mega-heuristic Approach to the Problem of Component Identification in Automated Knowledge Generation
Abstract
The ever-narrowing bottleneck in the knowledge acquisition process begs a solution. The ongoing Automated Knowledge Generation (AKG) research at the University of Central Florida is attempting to address this issue by developing techniques for the construction of a fully functional knowledge base given a CAD representation of a process control system. A major portion of this effort is the correct identification of components given relatively unconstrained descriptive information. The Parser subsystem of AKG, detailed here, interacts with the Component Knowledge Base to fulfill this purpose by utilizing a mega-heuristic approach coupled with a search mechanism guided by fixed and dynamic levels of inductive bias.
Notes
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Graduation Date
1989
Semester
Fall
Advisor
Gonzalez, Avelino J.
Degree
Master of Science (M.S.)
College
College of Engineering
Department
Computer Engineering
Format
Pages
149 p.
Language
English
Length of Campus-only Access
None
Access Status
Masters Thesis (Open Access)
Identifier
DP0026944
Subjects
Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic
STARS Citation
Kladke, Robin Rouch, "A Mega-heuristic Approach to the Problem of Component Identification in Automated Knowledge Generation" (1989). Retrospective Theses and Dissertations. 4168.
https://stars.library.ucf.edu/rtd/4168
Accessibility Status
Searchable text