Explanation-Based Learning for Diagnosis

Abstract

We present explanation-based learning (EBL) methods aimed at improving the performance of diagnosis systems integrating associational and model-based components. We consider multiple-fault model-based diagnosis (MBD) systems and describe two learning architectures. One, EBL ia , is a method for “learning in advance.” The other, EBL(p), is a method for “learning while doing.” EBL ia precompiles models into associations and relies only on the associations during diagnosis. EBL(p) performs compilation during diagnosis whenever reliance on previously learned associational rules results in unsatisfactory performance—as defined by a given performance threshold p . We present results of empirical studies comparing MBD without learning versus EBL ia and EBL(p). The main conclusions are as follows. EBL ia is superior when it is feasible, but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required.