Abstract
The paper addresses the problem of automated model acquisition through the re-use of fault knowledge. 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. To demonstrate DR's generality, it has also been applied to a coffee maker fault knowledge base to extract the component models of a full coffee device.
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Abu-Hakima, S. (1996). Automating model acquisition by fault knowledge re-use: Introducing the Diagnostic Remodeler algorithm. In: McCalla, G. (eds) Advances in Artifical Intelligence. Canadian AI 1996. Lecture Notes in Computer Science, vol 1081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61291-2_71
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DOI: https://doi.org/10.1007/3-540-61291-2_71
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