An improved diagnostic method for probabilistic consistency-based diagnosis

In consistency-based diagnosis (CBD), abnormal behavior is sorted out based on de- viation from a normal behavior specification. Probabilities have been added to CBD for quantifying uncertainty on, e.g., the behavior of faulty components. While resulting in more complete models, the requirement of such uncertainty parameters goes in opposition to the original CBD motivation. The conflict measure stands closer to CBD by comput- ing solutions without the need of priors on candidates, however, its results might not be suitable when only partial observations are available. In this paper, we propose a method called the diagnostic coefficient, which better solves the partial observability case, while needing the same parameters as the conflict measure. The diagnostic coefficient is based on the idea that observations are conflicting if the observed outputs are discrepant with respect to alternative outputs that could have been observed. We report experiments with logical circuits where the diagnostic coefficient shows promising results compared to the conflict measure under various settings with missing observations.