Explanation-based learning for diagnosis

Diagnostic expert systems constructed using traditional knowledge-engineering techniques identify malfunctioning components using rules that associate symptoms with diagnoses. Model-based diagnosis (MBD) systems use models of devices to find faults given observations of abnormal behavior. These approaches to diagnosis are complementary. We consider hybrid diagnosis systems that include both associational and model-based diagnostic components. We present results on explanation-based learning (EBL) methods aimed at improving the performance of hybrid diagnostic problem solvers. We describe two architectures called STATIC and EBL(p). STATIC pre-compiles models into associations, and at run-time the diagnostic system is purely associational. In EBL(p), the run-time diagnosis system is a hybrid: learned associational rules are preferred but the MBD component is activated whenever the performance falls below a threshold p. We present results of empirical studies comparing MBD without learning versus STATIC and EBL(p). The main conclusions are as follows. STATIC 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