D'Angelo, M, Ghahremani, S, Gerasimou, S et al. (4 more authors) (2020) Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning. In: 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), 17-21 Aug 2020, Washington, DC, USA. IEEE , pp. 121-126. ISBN 9781728184142
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Electronic mail, Cognition, Adaptive systems, Systematics, Decision trees, Bibliographies, Complexity theory |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Apr 2021 09:14 |
Last Modified: | 22 Apr 2021 09:14 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/acsos-c51401.2020.00042 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173339 |