  With the increasing complexity of CPSs, their behavior and decisions become
increasingly difficult to understand and comprehend for users and other
stakeholders. Our vision is to build self-explainable systems that can, at
run-time, answer questions about the system's past, current, and future
behavior. As hitherto no design methodology or reference framework exists for
building such systems, we propose the MAB-EX framework for building
self-explainable systems that leverage requirements- and explainability models
at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain
behavior of a system, then Build an explanation from explanation models and
convey this EXplanation in a suitable way to a stakeholder. We also take into
account that new explanations can be learned, by updating the explanation
models, should new and yet un-explainable behavior be detected by the system.

Artificial Intelligence