Formalizing the Abstraction Process in Model-Based Diagnosis

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Several theories have been proposed to capture the essence of abstraction. Among these, the \(\mathcal{KRA}\) model offers a framework where a set of generic abstraction operators allows abstraction to be automated.

In this paper we show how to describe component-based abstraction for the Model-Based Diagnosis task within the \(\mathcal{KRA}\) framework, and we discuss the benefits of such a formalization.

The clear and explicit partition of the system model into different levels required by \(\mathcal{KRA}\) (going from the perception level up to the theory level) opens the way to explore richer and better founded kinds of abstraction to apply to the MBD task.

Another noticeable advantage is that, by suitably personalizing the generic abstraction operators of \(\mathcal{KRA}\) , the whole abstraction process, from the definition of abstract (macro)components to the computation of their behaviors starting from those of the ground components, can be performed automatically in such a way that important relationships between ground and abstract diagnoses are guaranteed.