Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning
This work addresses the problem of managing complexity in CAS design for system engineers, but it is incremental as it applies existing data-driven methods to a new domain.
The paper tackles the challenge of engineering collective adaptive systems (CAS) with learning capabilities by introducing a systematic approach to reason about design choices and patterns, using data-driven methodologies like clustering and decision trees on literature review data to support cost-effective design-phase management.
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.