# Towards Self-Explainable Cyber-Physical Systems

arXiv: https://arxiv.org/abs/1908.04698 · HTML: https://mlsanity.com/paper/1908.04698/

## Key facts

- Cited by 5 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 13 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [SoK: Explainable Machine Learning for Computer Security Applications](https://mlsanity.com/paper/2208.10605/) (2022)
- [From Facts to Foils: Designing and Evaluating Counterfactual Explanations for Smart Environments](https://mlsanity.com/paper/2510.03078/) (2025)
- [Context-aware, Ante-hoc Explanations of Driving Behaviour](https://mlsanity.com/paper/2511.14428/) (2025)
- [Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions](https://mlsanity.com/paper/2606.09568/) (2026)
- [Behavior-Driven Explainability](https://mlsanity.com/paper/2607.24881/) (2026)

## Abstract (arXiv)

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.