# Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning

arXiv: https://arxiv.org/abs/2008.03995 · HTML: https://mlsanity.com/paper/2008.03995/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation](https://mlsanity.com/paper/2201.03947/) (2022)

## Abstract (arXiv)

Engineering collective adaptive systems (CAS) with learning capabilities is a challenging task due to their multi-dimensional and complex design space. Data-driven approaches for CAS design could introduce new insights enabling system engineers to manage the CAS complexity more cost-effectively at the design-phase. This paper introduces a systematic approach to reason about design choices and patterns of learning-based CAS. Using data from a systematic literature review, reasoning is performed with a novel application of data-driven methodologies such as clustering, multiple correspondence analysis and decision trees. The reasoning based on past experience as well as supporting novel and innovative design choices are demonstrated.