Recent research in the Knowledge Acquisition (KA) field, centers on defining a formal methodology for the KA process. This research includes the following goals: automating the KA process to decrease the KA time constraint; applying psychological techniques to extract the underlying structure of the expert's knowledge; and defining expertise in terms of "generic tasks" to yield possible knowledge organizations and strategies for the implementation of the expert system. This thesis provides an overview of the benefits and concerns of an automated KA system, psychological scaling techniques as they apply to KA, and the relevance of generic tasks. A generic task defines a knowledge type and organization, and a control strategy that characterizes a component of an expert system. This thesis also includes the design and implementation of a Knowledge Acquisition Tool for Identification of Generic Tasks. This tool provides an interface to the expert for the initial KA encounter. Using psychological techniques, the tool extracts a list of the main concepts of expertise, and elicits a rating from the expert comparing the similarity of each of these concepts to generic task concepts. The results become inputs to a clustering technique that organize the concepts into the generic tasks. The result of any concepts that do not cluster could identify a previously undefined generic task. The implementation is in the C language, accessing the FASTCLUS procedure of the SAS software package for VAX hardware.
Library of Congress Subject Headings
Knowledge acquisition (Expert systems); Expert systems (Computer science)--Design; Knowledge acquisition (Expert systems)--Industrial applications
Department, Program, or Center
Computer Science (GCCIS)
Buck, Arlene J., "Automated knowledge acquisition tool for identification of generic tasks" (1990). Thesis. Rochester Institute of Technology. Accessed from
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