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

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