Parametric Abstraction of Behavioral Modes for Model-Based Diagnosis

In the present paper we address the problem of automatically abstracting the behavioral modes of system components on the basis of their indiscriminability in a diagnostic setting. Our goal is to abstract the original model in such a way as to provide more informative results to the supervisor, without loosing any relevant diagnostic information. This paper extends and complements existing work on automatic abstraction in MBD in different directions: we propose a framework to integrate different parameters (system observability, context restriction and status restriction) that can influence the abstractions; we develop an algorithm for the computation of abstractions that can take advantage of the symbolic compilation of the system model for giving both theoretical guarantees about the computational cost and good experimental performance on non-trivial domains; finally, we discuss the properties of the abstractions resulting by the combination of a-priori, user-provided abstractions with the ones automatically computed by our algorithm