Supporting Decision-Making in Diagnosis of Discrete-Event Systems by Model-Based Temporal Techniques

Decision support is essential when humans are responsible for choosing critical courses of action about large, complex, distributed, partially-observable, dynamical systems. When the functioning of the system looks abnormal, a decision is expected to be taken based on the root cause of the undesired behavior. Still, several alternative root causes, or candidates, each consisting of a set of faults, can explain the same observation(s). Since finding such candidates requires heavy diagnostic reasoning, the literature describes a vast collection of automated diagnosis tools. When the diagnosis tool is model-based, the knowledge stored in the system model is drawn not only from human experts but also from design and/or operation data. Up to a few years ago, model-based diagnosis was set-oriented, a candidate being a set of faults that accounts for the observation(s). Recently, a temporal-oriented perspective to diagnosis of dynamical systems was proposed: a candidate has become a chronological sequence of faults, and the set of all candidates has turned into a regular language over the alphabet whose symbols are faults. This new perspective may help a human operator to better understand what actually took place inside the system, thus supporting the decision-making process more adequately. This chapter deals with the decision support provided by a model-based temporal-oriented approach to diagnosis of partially-observable discrete-event systems. A (distributed) discrete-event system consists of components that are modeled as communicating automatons. Three temporal-oriented diagnosis techniques, which differ for the amount of compiled knowledge they manage (if any), are investigated: interpreted diagnosis, compiled diagnosis, and hybrid diagnosis. Experimental results suggest that both interpreted and compiled diagnoses suffer from serious complexity difficulties, while hybrid diagnosis may be applicable in real contexts.