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Titel: Reciprocal Learning in Production and Logistics
VerfasserIn: Nixdorf, Steffen
Zhang, Minqi
Ansari, Fazel
Grosse, Eric H.
Sprache: Englisch
Titel: IFAC-PapersOnLine
Bandnummer: 55
Heft: 10
Seiten: 854-859
Verlag/Plattform: Elsevier
Erscheinungsjahr: 2022
Freie Schlagwörter: Human-Machine Symbiosis
Industry 4.0
Reciprocal Learning
Work-Based Learning
DDC-Sachgruppe: 330 Wirtschaft
Dokumenttyp: Konferenzbeitrag (in einem Konferenzband / InProceedings erschienener Beitrag)
Abstract: Integration of AI technologies and learnable systems in production and logistics transforms the concepts of work organization and task assignments to human and machine agents. Thus, the question arises of what intelligent machines and human workers may be able to achieve as teammates. One answer may be guiding and training the workforce at the workplace to cope with emerging skill mismatches, emphasized by concepts of work-based learning. The extension of cyber-physical production systems towards becoming human-centered and social systems enabling human-machine interaction, creates opportunities for human-machine symbiosis by complementing each other's strengths. In this way, the concept of “Reciprocal Learning” (RL) between humans and intelligent machines has emerged, which is still rather ambiguous and lacks a profound knowledge base. Especially in production and logistics, literature is fragmented. Hence, the objective of this paper is to conduct a systematic literature review to elicit and cluster the knowledge base in RL represented by adjacent interdisciplinary fields of research, such as social and computer sciences. This work contributes to the literature by developing a comprehensive knowledge base on the concept of RL enabling to pursue future research directions towards the realization of human-machine symbiosis through RL in production and logistics.
DOI der Erstveröffentlichung: 10.1016/j.ifacol.2022.09.519
URL der Erstveröffentlichung: https://doi.org/10.1016/j.ifacol.2022.09.519
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-387663
hdl:20.500.11880/34929
http://dx.doi.org/10.22028/D291-38766
ISSN: 2405-8963
Datum des Eintrags: 19-Jan-2023
Bemerkung/Hinweis: 10th IFAC Conference on Manufacturing Modelling, Management and Control, IFAC MIM 2022, Nantes, France : pp. 854-859
Fakultät: HW - Fakultät für Empirische Humanwissenschaften und Wirtschaftswissenschaft
Fachrichtung: HW - Wirtschaftswissenschaft
Professur: HW - Prof. Dr. Eric Grosse
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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