Learning multiple fault diagnosis
Yousri El Fattah, Paul O’Rorke · 2002
The authors describe two methods for integrating model-based diagnosis (MBD) and explanation-based learning (EBL). The first method, EBL, uses a generate-test-debug paradigm, generating diagnostic hypotheses using learned associational rules that summarize model-based diagnostic experiences. This strategy is a form of learning while doing model-based troubleshooting and could be called online learning. The second diagnosis and learning method described here (EBL-STATIC) involves learning in advance. Learning begins in a training phase prior to performance or testing. Empirical results of computational experiments comparing the learning methods with MBD on two devices (the polybox and the binary full adder) are reported. For the same diagnostic performance, EBL-STATIC is several orders of magnitude faster than MBD, while EBL can cause performance slow-down.>