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Autonomously Improving Systems in Industry: A Systematic Literature Review
Chalmers University of Technology.
Chalmers University of Technology.
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-7700-1816
2021 (English)In: Software Business: 11th International Conference, ICSOB 2020, Karlskrona, Sweden, November 16–18, 2020, Proceedings / [ed] Eriks Klotins; Krzysztof Wnuk, Springer, 2021, p. 30-45Conference paper, Published paper (Refereed)
Abstract [en]

A significant amount of research effort is put into studying machine learning (ML) and deep learning (DL) technologies. Real-world ML applications help companies to improve products and automate tasks such as classification, image recognition and automation. However, a traditional “fixed” approach where the system is frozen before deployment leads to a sub-optimal system performance. Systems autonomously experimenting with and improving their own behavior and performance could improve business outcomes but we need to know how this could actually work in practice. While there is some research on autonomously improving systems, the focus on the concepts and theoretical algorithms. However, less research is focused on empirical industry validation of the proposed theory. Empirical validations are usually done through simulations or by using synthetic or manually alteration of datasets. The contribution of this paper is twofold. First, we conduct a systematic literature review in which we focus on papers describing industrial deployments of autonomously improving systems and their real-world applications. Secondly, we identify open research questions and derive a model that classifies the level of autonomy based on our findings in the literature review. 

Place, publisher, year, edition, pages
Springer, 2021. p. 30-45
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 407
Keywords [en]
AI engineering, Autonomously improving systems, Empirical validation, Industrial application, Machine learning, Deep learning, Image recognition, Technology transfer, Industrial deployment, Level of autonomies, Literature reviews, Optimal system performance, Research questions, Systematic literature review, Theoretical algorithms, Image enhancement
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:mau:diva-48966DOI: 10.1007/978-3-030-67292-8_3Scopus ID: 2-s2.0-85101387919ISBN: 978-3-030-67291-1 (print)ISBN: 978-3-030-67292-8 (electronic)OAI: oai:DiVA.org:mau-48966DiVA, id: diva2:1623068
Conference
11th International Conference, ICSOB 2020, Karlskrona, Sweden, November 16–18, 2020
Available from: 2021-12-27 Created: 2021-12-27 Last updated: 2022-04-19Bibliographically approved

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Olsson, Helena Holmström

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