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arXiv · 2008.03995

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

Mirko D'Angelo·Sona Ghahremani·Simos Gerasimou·Johannes Grohmann·Ingrid Nunes·Sven Tomforde·Evangelos Pournaras
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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.

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Mirko D'Angelo, Sona Ghahremani, Simos Gerasimou, Johannes Grohmann, Ingrid Nunes, Sven Tomforde, Evangelos Pournaras. 2020-08-10. Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning. https://arxiv.org/abs/2008.03995

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