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Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systemsRule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.
Document ID
19890006216
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Stclair, D. C.
(Missouri Univ. Rolla., United States)
Sabharwal, C. L.
(Missouri Univ. Rolla., United States)
Bond, W. E.
(McDonnell-Douglas Research Labs. Saint Louis, MO., United States)
Hacke, Keith
(McDonnell-Douglas Research Labs. St. Louis, MO., United States)
Date Acquired
September 5, 2013
Publication Date
October 1, 1988
Publication Information
Publication: NASA, Marshall Space Flight Center, Fourth Conference on Artificial Intelligence for Space Applications
Subject Category
Computer Programming And Software
Accession Number
89N15587
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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