Model based reasoning has been the main area of investigation of our group in recent years. Several aspects have been explored.

From a methodological point of view, the recent contributions can be grouped as follows:

• Analysis of the problems arising when modeling and diagnosing complex dynamic systems. In particular, we focused on the study of the problem that have to be faced when modeling complex real world system with controlled dynamic behavior.

• Modelling with Process algebras. We investigated the use of the formalism of Process algebras (which is widely used for modeling in other areas of computer science) which proved to be a very flexible and powerful language for model-based reasoning.

• Integration of diagnosis in the design process of physical devices. If one analyses the design process of any significant complex systems, it is very common to see that diagnostic issues are usually taken into account only at the end of the process and are not integrated with the rest of the process. In particular, during the critical phases of the design process, when the actual architecture of the system is conceptualized, the control strategies are defined and models or prototypes of the system are simulated, diagnostic issues are not taken into account. Not only does this mean that the diagnostic software is not developed together with the control software, but, more critically, that issues such as the diagnosability of the system being designed or the analysis of the FMEA (Failure Modes Effect Analysis, which is very useful to discover safety critical faults or failures) are seldom and only partially considered. The goal of our work is to define defining a new process in which these issues are integrated within the design of a system and of its control strategies. The project also aims at defining and implementing a software toolkit supporting the new process. The toolkit integrates applications for design and simulation (e.g., Matlab Simulink) and model-based reasoning systems for diagnosis-related tasks.

• Embedding model-based diagnosis into automotive systems. The goal is to study how MBR technologies can be used to develop the diagnostic software that is emebdeed into real complex systems, especially in the automotive field.

• Applying model-based reasoning to teh generation of teh Failure Mode Effeft and Criticality Analysis (FMECA) in aeronautic domain. The goal is to provide methodology supporting technicians in the analysis of the effect that fault may have on components of aircraft, in particular for verifying safety and criticality requirements.

• Multi-modal reasoning and multiple representations for diagnostic problem solving. During 2002 we have continued to investigate how to integrate different reasoning mechanisms and multiple representations in order to solve in a more efficient ways diagnostic problems. We have performed experiments for evaluating the integration of Case based reasoning approach with the model based reasoning one. We have also developed the capability of diagnostic strategies able to select the most appropriate model of the domain on the basis of the manifestations available for solving the diagnostic problem.

• Automatic abstraction of System Models. We have studied methods which exploit reductions in the system observability in order to automatically synthesize abstract System Models which are simpler than the original model but retain the same diagnostic discrimination power. In particular, we have developed an algorithm which, given an hypothesis on the current available observables and their granularity,
• produces an abstract model where the behavioral modes of system components which have become indistinguishable due to the reduced observability are merged into abstract behavioral modes. Abstract models produced under different hypotheses about the system observability can be stored in a library and appropriately selected at run-time in order to improve diagnostic efficiency and return fewer, more general diagnoses when the system observability is reduced (e.g. due to the impossibility of taking manual measures or due to sensors failure)

Two are the he main areas of application for the MBR techniques described above: automotive and avionics. In 2002, in fact we participated actively in two European Projects:

• The IDD (Integrating Diagnosis and Design). The project is supported by the EU under the Substainable Growth Programme (V Framework) and the partners are: Centro Ricerche Fiat, Daimler Chrysler, Renault, PSA - Peugeout Citroen, Magneti Marelli, Occ'm, Universitè Paris XIII, Technical University Munchen, Università di Torino. The aim of the project is to integrate model-based diagnostic techniques in the tools used during the design of a system (CAD tools, simulation tools such as Matlab/Simulink). In this way the diagnosability of a system can be verified (and improved) during he design process and the diagnostic rules to be implemented in the control of the system can beproduced automatically at the end of the design.

• The AUTAS (Automatic FMECA for aircraft systems). he project is supported by the EU under the Sustainable Growth Programme (V Framework) and the partners are: Alenia, Eurocopter, Israel Aircraft Industries, Dutch National Aerospace Laboratory NLR, Sofrten, Occ'm, Technical University Munchen, Università di Torino. The aim of the project is the application of model based reasoning to the of the effects of faults in the behavior of aircraft systems.

Another relevant domain for diagnostic problem solving concerns autonomous robots for space applications. In a project supported by ASI (Agenzia Spaziale Italiana) we have developed the diagnostic agent by taking as a test bed the SPIDER robotic arm. In this framework we have extended the previous version of diagnostic agent (based on the techniques developed for a concise representation of the diagnosis) by adding a component able to derive a more abstract model of the Robotic Arm when the set of manifestations is reduced (for example only sensorized data are available) . In the current version, the diagnostic agent is able to select the domain model that best suits the current sets of manifestations and to perform the diagnosis at that level of abstraction with a significant reduction of the number of diagnoses.