An Approach for the Validation of Fault-based Knowledge Through the Automated Generation of Model-based Functional Knowledge.

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ConferenceProceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96) Workshop on Validation and Verification of Knowledge Based Systems and Subsystems, August 4-8, 1996., Portland, Oregon, USA
AbstractThe paper addresses the problem of validation of fault knowledge through automated model acquisition. The Diagnostic Remodeler (DR) algorithm has been implemented for the automated generation of behavioural component models with an explicit representation of function by re-using fault-based knowledge. DR re-uses as its first application the fault knowledge of the Jet Engine troubleshooting Assistant (JETA). DR extracts a model of the Main Fuel System using real-world engine fault knowledge and two types of background knowledge as input: device dependent and device independent background knowledge. The generated model uncovers gaps and inconsistencies in the fault-based knowledge. To demonstrate DR's generality, it was applied to coffee maker fault knowledge to extract the component models of a full coffee device. It is possible to use DR as a general means of validating fault knowledge.
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AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number39215
NPARC number5764638
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Record identifierdd0f086f-1300-4b32-9b9f-014d8ae0c29c
Record created2009-03-29
Record modified2016-05-09
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