# Uncertainty in Self-Adaptive Systems: A Research Community Perspective

arXiv: https://arxiv.org/abs/2103.02717 · HTML: https://mlsanity.com/paper/2103.02717/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2021.

## Built on by (most cited first, 6 of 6)

- [Beyond Task Completion: An Assessment Framework for Evaluating Agentic AI Systems](https://mlsanity.com/paper/2512.12791/) (2025)
- [Lifelong Self-Adaptation: Self-Adaptation Meets Lifelong Machine Learning](https://mlsanity.com/paper/2204.01834/) (2022)
- [Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems using Lifelong Self-Adaptation](https://mlsanity.com/paper/2211.02658/) (2022)
- [Accelerating Policy Synthesis in Large-Scale MDPs via Hierarchical Adaptive Refinement](https://mlsanity.com/paper/2506.17792/) (2025)
- [Autonomous Learning From Success and Failure: Goal-Conditioned Supervised Learning with Negative Feedback](https://mlsanity.com/paper/2509.03206/) (2025)
- [CRRL: A Causality-Based Reinforcement Learning Framework for Autonomous System Recovery](https://mlsanity.com/paper/2607.03177/) (2026)

## Abstract (arXiv)

One of the primary drivers for self-adaptation is ensuring that systems achieve their goals regardless of the uncertainties they face during operation. Nevertheless, the concept of uncertainty in self-adaptive systems is still insufficiently understood. Several taxonomies of uncertainty have been proposed, and a substantial body of work exists on methods to tame uncertainty. Yet, these taxonomies and methods do not fully convey the research community's perception on what constitutes uncertainty in self-adaptive systems and how to tackle it. To understand this perception and learn from it, we conducted a survey comprising two complementary stages. In the first stage, we focused on current research and development. In the second stage, we focused on directions for future research. The key findings of the first stage are: a) an overview of uncertainty sources considered in self-adaptive systems, b) an overview of existing methods used to tackle uncertainty in concrete applications, c) insights into the impact of uncertainty on non-functional requirements, d) insights into different opinions in the perception of uncertainty within the community, and the need for standardised uncertainty-handling processes to facilitate uncertainty management in self-adaptive systems. The key findings of the second stage are: a) the insight that over 70% of the participants believe that self-adaptive systems can be engineered to cope with unanticipated change, b) a set of potential approaches for dealing with unanticipated change, c) a set of open challenges in mitigating uncertainty in self-adaptive systems, in particular in those with safety-critical requirements. From these findings, we outline an initial reference process to manage uncertainty in self-adaptive systems.