# Lifelong Self-Adaptation: Self-Adaptation Meets Lifelong Machine Learning

arXiv: https://arxiv.org/abs/2204.01834 · HTML: https://mlsanity.com/paper/2204.01834/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 63 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [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)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Toward Understanding Catastrophic Forgetting in Continual Learning](https://mlsanity.com/paper/1908.01091/) (2019)
- [Uncertainty in Self-Adaptive Systems: A Research Community Perspective](https://mlsanity.com/paper/2103.02717/) (2021)
- [On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems](https://mlsanity.com/paper/2103.10194/) (2021)

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

- [Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems using Lifelong Self-Adaptation](https://mlsanity.com/paper/2211.02658/) (2022)
- [Online ML Self-adaptation in Face of Traps](https://mlsanity.com/paper/2309.05805/) (2023)

## Abstract (arXiv)

In the past years, machine learning (ML) has become a popular approach to support self-adaptation. While ML techniques enable dealing with several problems in self-adaptation, such as scalable decision-making, they are also subject to inherent challenges. In this paper, we focus on one such challenge that is particularly important for self-adaptation: ML techniques are designed to deal with a set of predefined tasks associated with an operational domain; they have problems to deal with new emerging tasks, such as concept shift in input data that is used for learning. To tackle this challenge, we present textitlifelong self-adaptation: a novel approach to self-adaptation that enhances self-adaptive systems that use ML techniques with a lifelong ML layer. The lifelong ML layer tracks the running system and its environment, associates this knowledge with the current tasks, identifies new tasks based on differentiations, and updates the learning models of the self-adaptive system accordingly. We present a reusable architecture for lifelong self-adaptation and apply it to the case of concept drift caused by unforeseen changes of the input data of a learning model that is used for decision-making in self-adaptation. We validate lifelong self-adaptation for two types of concept drift using two cases.