Knowledge Base Refinement Using Abstract Control Knowledge

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Citation: David C. Wilkins and William J. Clancey and Bruce G. Buchanan. (1987) Knowledge Base Refinement Using Abstract Control Knowledge. In KSL-87-01, January,1987.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author David C. Wilkins and William J. Clancey and Bruce G. Buchanan
title Knowledge Base Refinement Using Abstract Control Knowledge
number KSL-87-01
institution Knowledge Systems, AI Laboratory
year 1987
month January
Bibtex more
note STAN-CS-87-1182 9 pages.
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abstract An explicit representation of the problem solving method of an expert system shell as an abstract control knowledge provides a powerful foundation for learning. This paper describes the abstract control knowledge of the HERACLES expert system shell for heuristic classification problems, and describes how the ODYSSEUS apprenticeship learning program uses this representation to semi-automate "end game" knowledge acquisition. The problem solving method of HERACLES is represented explicitly as domain-independent tasks and metarules. Metarules locate and apply domain knowledge to achieve problem solving subgoals, such as testing, refining, or differentiating between hypothesis; and asking general or clarifying questions.We show how monitoring abstract control knowledge for metarule premise failures provides a means of detecting gaps in the knowledge base. A knowledge base gap will almost always cause a metarule premise failure. We aslo show how abstract control knowledge plays a crucial role in using underlying domain theories for learning, especially weak domain theories. The construction of abstract control knowledge requires that the different types of knowledge that enter into problem solving be represented in different knowledge relations. This provides a foundation for the integration of underlying domain theories into a learning system, because justification of different types of new knowledge usually requires different ways of using an underlying domain theory. We advocate the construction of a definitional constraint for each knowledge relation that specifies how the relation is defined and justified in terms of underlying domain theories.

KSL Technical Report ID: KSL-87-01
Facts about Knowledge Base Refinement Using Abstract Control KnowledgeRDF feed
Abstract An explicit representation of the problem … An explicit representation of the problem solving method of an expert system shell as an abstract control knowledge provides a powerful foundation for learning. This paper describes the abstract control knowledge of the HERACLES expert system shell for heuristic classification problems, and describes how the ODYSSEUS apprenticeship learning program uses this representation to semi-automate "end game" knowledge acquisition. The problem solving method of HERACLES is represented explicitly as domain-independent tasks and metarules. Metarules locate and apply domain knowledge to achieve problem solving subgoals, such as testing, refining, or differentiating between hypothesis; and asking general or clarifying questions.We show how monitoring abstract control knowledge for metarule premise failures provides a means of detecting gaps in the knowledge base. A knowledge base gap will almost always cause a metarule premise failure. We aslo show how abstract control knowledge plays a crucial role in using underlying domain theories for learning, especially weak domain theories. The construction of abstract control knowledge requires that the different types of knowledge that enter into problem solving be represented in different knowledge relations. This provides a foundation for the integration of underlying domain theories into a learning system, because justification of different types of new knowledge usually requires different ways of using an underlying domain theory. We advocate the construction of a definitional constraint for each knowledge relation that specifies how the relation is defined and justified in terms of underlying domain theories. ed in terms of underlying domain theories.
Author David C. Wilkins and William J. Clancey and Bruce G. Buchanan  +
Bibtype techreport  +
Has author David C. Wilkins and William J. Clancey and Bruce G. Buchanan  +
Has identifier KSL-87-01  +
Has publishing details January,1987  +
Has title Knowledge Base Refinement Using Abstract Control Knowledge  +
Has where published KSL-87-01  +
Has year 1987  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-87-01  +
Month January  +
Note STAN-CS-87-1182 9 pages.
Number KSL-87-01  +
Process note YES  +
Title Knowledge Base Refinement Using Abstract Control Knowledge  +
Year 1987  +
Retrieved from "http://tw.rpi.edu/wiki/Knowledge_Base_Refinement_Using_Abstract_Control_Knowledge"
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