MEDITO: A Logic-Based Meta-diagnosis Tool

In every Model-Based Diagnosis (MBD) approach, a model of a real-world system and some observations of such a system are used by a diagnostic algorithm to compute diagnoses. Contrary to MBD classical hypotheses, real-world applications provide us with empirical data suggesting that diagnostic systems, i.e. a model, observations and a diagnostic algorithm, are sometimes abnormal with respect to some required properties. This is where Meta-Diagnosis comes into play with a theory to determine abnormalities in diagnostic systems. Unfortunately, Artificial Intelligence lacks of a tool putting meta-diagnosis theory to practice. Our first contribution in this paper is such a tool, called MEDITO. Moreover, we provide a real-world example of MEDITO´s application at meta-diagnosing an Airbus landing gear extraction and retraction system with successful results.