%0 Generic %1 DaGhGeGrNuToPo2020-eCas-LearningInCAS %A D'Angelo, Mirko %A Ghahremani, Sona %A Gerasimou, Simos %A Grohmann, Johannes %A Nunes, Ingrid %A Tomforde, Sven %A Pournaras, Evangelos %B 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) %D 2020 %I IEEE %K Statistical_estimation_and_machine_learning descartes myown t_workshop %P 121--126 %R 10.1109/ACSOS-C51401.2020.00042 %T Learning to Learn in Collective Adaptive Systems: Mining Design Pattern for Data-driven Reasoning %U https://doi.org/10.1109/ACSOS-C51401.2020.00042 %X Engineering collective adaptive systems (CAS) with learning capabilities is a challenging task due to their multidimensional 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.

@conference{DaGhGeGrNuToPo2020-eCas-LearningInCAS, abstract = {Engineering collective adaptive systems (CAS) with learning capabilities is a challenging task due to their multidimensional 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.}, added-at = {2020-09-04T11:44:55.000+0200}, author = {D'Angelo, Mirko and Ghahremani, Sona and Gerasimou, Simos and Grohmann, Johannes and Nunes, Ingrid and Tomforde, Sven and Pournaras, Evangelos}, biburl = {https://www.bibsonomy.org/bibtex/29f7de144edac85642b688c3926d8e26f/se-group}, booktitle = {2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)}, doi = {10.1109/ACSOS-C51401.2020.00042}, interhash = {9e2c57f8e5a95f730e9cfdf82248882f}, intrahash = {9f7de144edac85642b688c3926d8e26f}, keywords = {Statistical_estimation_and_machine_learning descartes myown t_workshop}, pages = {121--126}, publisher = {IEEE}, timestamp = {2021-01-12T13:44:31.000+0100}, title = {Learning to Learn in Collective Adaptive Systems: Mining Design Pattern for Data-driven Reasoning}, url = {https://doi.org/10.1109/ACSOS-C51401.2020.00042}, year = 2020 }