Model-based diagnosis with uncertain observations

Classical model-based diagnosis uses a model of the system to infer diagnoses—explanations—of a given abnormal observation. In this study we address the case where there is uncertainty over a given observation and explore it. This can happen, for example, when the observation outputs are collected by sensors with some noise, that are known to return incorrect value with some probability. We formally define this problem for abductive and consistency-based forms domains. Furthermore, we propose two complete and sound algorithms for finding and ranking all diagnoses and analyze their complexity. We dive deeper and improve the first algorithm's efficiency even more by exploiting past results. Finally, we propose a third algorithm that returns the most likely diagnosis without finding all possible diagnoses, assuming the uncertainty over the observations and the components behavior modes are independents. Experimental evaluation shows that the last algorithm can be very efficient in cases where the diagnosis cardinality, as well as the uncertainty likelihood over the observation, are expected to be small. If, however, all possible diagnoses are desired, then the choice between the first two algorithms depends on whether the domain's diagnosis form is abductive or consistent.