Towards Self-Explainable Cyber-Physical Systems
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Aug 2019.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2; 2026: 2.
5 papers.
- 2022SoK: Explainable Machine Learning for Computer Security Applicationscited by 20
- 2025From Facts to Foils: Designing and Evaluating Counterfactual Explanations for Smart Environmentscited by 0
- 2025Context-aware, Ante-hoc Explanations of Driving Behaviourcited by 0
- 2026Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directionscited by 0
- 2026Behavior-Driven Explainabilitycited by 0
- 2026Behavior-Driven Explainability27 Jul 2026
- 2026Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions8 Jun 2026
- 2025Context-aware, Ante-hoc Explanations of Driving Behaviour18 Nov 2025
- 2025From Facts to Foils: Designing and Evaluating Counterfactual Explanations for Smart Environments3 Oct 2025
- 2022SoK: Explainable Machine Learning for Computer Security Applications22 Aug 2022