The aim of the DISCO team is to develop broad-spectrum methodological research in the field of diagnostics. The team's scientific skills lie at the frontiers of Automatic Control and Artificial Intelligence, and its focus is on abductive reasoning applied to a wide variety of system classes (static systems, dynamic systems: discrete, continuous or hybrid). The fundamental principle of a diagnostic process is to compare uncertain/certain observation of a real system (noisy measurements, alarms, messages, tests) with the available knowledge of this system (certain/uncertain models) in order to establish a state of health. This research is motivated by the fact that diagnosis is crucial to improving, among other things, the safety, resilience and maintainability of systems. DISCO's activities in carrying out this research can be broken down into three types.

1. Formal study of diagnostic properties (diagnosability, identifiability, etc.) in dynamic systems (discrete, continuous, hybrid).
2. Development of diagnostic methods and algorithms (diagnosers) for the classes of systems studied, with the production of software and demonstrators.
3. Study of specific applications in partnership with industry (aeronautics, agriculture, automotive, medicine, space).