A Symbolic Approach for Component Abstraction in Model-Based Diagnosis

In the present paper we address the problem of automatically synthesizing component abstractions by taking into account the level of observability of the system as well as contextual conditions within the context of the Model-Based Diagnosis task. We show how to exploit the notion of indiscriminability for abstracting the original model without loosing any relevant diagnostic information. The paper presents an algorithm for the computation of abstractions that takes advantage of the symbolic compilation of the system model for giving both theoretical guarantees about the computational cost and good experimental performance on a nontrivial domain.