Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning
Software Engineering
2020-08-11 v1
Abstract
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.
Cite
@article{arxiv.2008.03995,
title = {Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning},
author = {Mirko D'Angelo and Sona Ghahremani and Simos Gerasimou and Johannes Grohmann and Ingrid Nunes and Sven Tomforde and Evangelos Pournaras},
journal= {arXiv preprint arXiv:2008.03995},
year = {2020}
}