# Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems using Lifelong Self-Adaptation

arXiv: https://arxiv.org/abs/2211.02658 · HTML: https://mlsanity.com/paper/2211.02658/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 92 references that are in the map.
- First posted on arXiv 4 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://mlsanity.com/paper/2011.03395/) (2020)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Toward Understanding Catastrophic Forgetting in Continual Learning](https://mlsanity.com/paper/1908.01091/) (2019)
- [Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review](https://mlsanity.com/paper/2103.04112/) (2021)
- [Uncertainty in Self-Adaptive Systems: A Research Community Perspective](https://mlsanity.com/paper/2103.02717/) (2021)
- [ActivFORMS: A Formally-Founded Model-Based Approach to Engineer Self-Adaptive Systems](https://mlsanity.com/paper/1908.11179/) (2019)
- [The Vision of Self-Evolving Computing Systems](https://mlsanity.com/paper/2204.06825/) (2022)
- [Lifelong Self-Adaptation: Self-Adaptation Meets Lifelong Machine Learning](https://mlsanity.com/paper/2204.01834/) (2022)
- [Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-Adaptive Systems](https://mlsanity.com/paper/2204.06254/) (2022)

## Built on by (most cited first, 2 of 2)

- [Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap](https://mlsanity.com/paper/2512.04680/) (2025)
- [Towards Neuro-symbolic Causal Rule Synthesis, Verification, and Evaluation Grounded in Legal and Safety Principles](https://mlsanity.com/paper/2604.28087/) (2026)

## Abstract (arXiv)

Recently, machine learning (ML) has become a popular approach to support self-adaptation. ML has been used to deal with several problems in self-adaptation, such as maintaining an up-to-date runtime model under uncertainty and scalable decision-making. Yet, exploiting ML comes with inherent challenges. In this paper, we focus on a particularly important challenge for learning-based self-adaptive systems: drift in adaptation spaces. With adaptation space we refer to the set of adaptation options a self-adaptive system can select from at a given time to adapt based on the estimated quality properties of the adaptation options. Drift of adaptation spaces originates from uncertainties, affecting the quality properties of the adaptation options. Such drift may imply that eventually no adaptation option can satisfy the initial set of the adaptation goals, deteriorating the quality of the system, or adaptation options may emerge that allow enhancing the adaptation goals. In ML, such shift corresponds to novel class appearance, a type of concept drift in target data that common ML techniques have problems dealing with. To tackle this problem, we present a novel approach to self-adaptation that enhances learning-based self-adaptive systems with a lifelong ML layer. We refer to this approach as lifelong self-adaptation. The lifelong ML layer tracks the system and its environment, associates this knowledge with the current tasks, identifies new tasks based on differences, and updates the learning models of the self-adaptive system accordingly. A human stakeholder may be involved to support the learning process and adjust the learning and goal models. We present a general architecture for lifelong self-adaptation and apply it to the case of drift of adaptation spaces that affects the decision-making in self-adaptation. We validate the approach for a series of scenarios using the DeltaIoT exemplar.