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EXPECT: Explicit Representations for Flexible AcquisitionTo create more powerful knowledge acquisition systems, we not only need better acquisition tools, but we need to change the architecture of the knowledge based systems we create so that their structure will provide better support for acquisition. Current acquisition tools permit users to modify factual knowledge but they provide limited support for modifying problem solving knowledge. In this paper, the authors argue that this limitation (and others) stem from the use of incomplete models of problem-solving knowledge and inflexible specification of the interdependencies between problem-solving and factual knowledge. We describe the EXPECT architecture which addresses these problems by providing an explicit representation for problem-solving knowledge and intent. Using this more explicit representation, EXPECT can automatically derive the interdependencies between problem-solving and factual knowledge. By deriving these interdependencies from the structure of the knowledge-based system itself EXPECT supports more flexible and powerful knowledge acquisition.
Document ID
19970013784
Acquisition Source
Ames Research Center
Document Type
Other
Authors
Swartout, BIll
(University of Southern California Marina del Rey, CA United States)
Gil, Yolanda
(University of Southern California Marina del Rey, CA United States)
Date Acquired
August 17, 2013
Publication Date
June 1, 1995
Publication Information
Publication: Proceedings of the Ninth Knowledge Acquisition for Knowledge-Based Systems Workshop (KAW '95)
Subject Category
Documentation And Information Science
Report/Patent Number
ISI/RS-95-397
AD-A308790
Accession Number
97N17489
Funding Number(s)
CONTRACT_GRANT: NCC2-520
CONTRACT_GRANT: DABT63-91-C-0025
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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