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doi:10.22028/D291-38766
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 |
Dateien zu diesem Datensatz:
Datei | Beschreibung | Größe | Format | |
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1-s2.0-S2405896322018201-main.pdf | 667,88 kB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons