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

PDF

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

Accessibility Status

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