# Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning

arXiv: https://arxiv.org/abs/2103.10847 · HTML: https://mlsanity.com/paper/2103.10847/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 63 references that are in the map.
- First posted on arXiv 19 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Engineering Trustworthy Self-Adaptive Software with Dynamic Assurance Cases](https://mlsanity.com/paper/1703.06350/) (2017)
- [Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots](https://mlsanity.com/paper/1903.03920/) (2019)
- [A Hybrid Approach Combining Control Theory and AI for Engineering Self-Adaptive Systems](https://mlsanity.com/paper/2004.11793/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Privacy-Preserving Cloud Architecture for Distributed Machine Learning at Scale](https://mlsanity.com/paper/2512.10341/) (2025)
- [Learning-driven Zero Trust in Distributed Computing Continuum Systems](https://mlsanity.com/paper/2311.17447/) (2023)
- [Engineering Reliable Autonomous Systems: Challenges and Solutions](https://mlsanity.com/paper/2606.23760/) (2026)

## Abstract (arXiv)

Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation. Recently, we also observe a rapidly growing interest in applying machine learning (ML) to support different adaptation mechanisms. While MAPE and CT have particular characteristics and strengths to be applied independently, in this paper, we are concerned with the question of how these approaches are related with one another and whether combining them and supporting them with ML can produce better adaptive systems. We motivate the combined use of different adaptation approaches using a scenario of a cloud-based enterprise system and illustrate the analysis when combining the different approaches. To conclude, we offer a set of open questions for further research in this interesting area.