Schriftenreihe der Wissenschaftlichen Gesellschaft für Arbeits- und Betriebsorganisation (WGAB) e. V., 2022, 207-225
WGAB 2022 Gesamtband Plapper
Reciprocal Learning in Human-Machine Collaboration: A Multi-Agent System Framework in Industry 5.0

Bibtex

Cite as text

						@Inbook{Nixdorf+Ansari+Schlund,
							Cite-key = "Nixdorf2022wgab", 
							Year= "2022", 
							 
							 Volume= "Schriftenreihe der Wissenschaftlichen Gesellschaft für Arbeits- und Betriebsorganisation (WGAB) e. V.", 
							Pages= "207-225", 
							Journal   = "WGAB",
							 Title= "Reciprocal Learning in Human-Machine Collaboration: A Multi-Agent System Framework in Industry 5.0", 
							Author= "Steffen Nixdorf, Fazel Ansari, Sebastian Schlund
TU Wien
", 
							Doi= "https://doi.org/10.30844/WGAB_2022_11", 
							 Abstract= "The increasing skill mismatch in manufacturing workforce is raising demand for training opportunities to cope with advanced manufacturing systems. To maintain production and adjust quickly to technological transformation, innovative work-based learning approaches are emphasized.
Intelligent machines are becoming capable of interaction and collaboration with humans. They not only introduce a new type of learnable workforce to manufacturing but may open opportunities to enhance learning of all learners. Symbiotic relationships of humans and machines have wide potential for human-centric manufacturing (aka Industry 5.0). Moreover, connecting smart devices and deploying self-learning solutions is envisioned to increase flexibility of manufacturing, thus changing work division between humans and machines.
Where humans and machines collaborate, the term Reciprocal Learning has been coined to describe the process of bidirectional learning. While a definition of Reciprocal Learning exists the boundary conditions of the concept are still ambiguous. In this paper, a framework for Reciprocal Learning in Human-Machine Collaboration for Industry 5.0 is presented to clarify what it is and what it isn’t. The approach is based on a multi-agent system perspective, comprising human and machine agents. Finally, an outlook on future research is presented.", 
							 Keywords= "Reciprocal Learning, Work-Based Learning, Human-Machine Collaboration, Industry 5.0, Human-Machine Symbiosis", 
							}
					
Steffen Nixdorf, Fazel Ansari, Sebastian Schlund
TU Wien(2022): Reciprocal Learning in Human-Machine Collaboration: A Multi-Agent System Framework in Industry 5.0. Schriftenreihe der Wissenschaftlichen Gesellschaft für Arbeits- und Betriebsorganisation (WGAB) e. V.(2022), S. 207-225. Online: https://doi.org/10.30844/WGAB_2022_11 (Abgerufen 03.12.22)

Abstract

Abstract

The increasing skill mismatch in manufacturing workforce is raising demand for training opportunities to cope with advanced manufacturing systems. To maintain production and adjust quickly to technological transformation, innovative work-based learning approaches are emphasized. Intelligent machines are becoming capable of interaction and collaboration with humans. They not only introduce a new type of learnable workforce to manufacturing but may open opportunities to enhance learning of all learners. Symbiotic relationships of humans and machines have wide potential for human-centric manufacturing (aka Industry 5.0). Moreover, connecting smart devices and deploying self-learning solutions is envisioned to increase flexibility of manufacturing, thus changing work division between humans and machines. Where humans and machines collaborate, the term Reciprocal Learning has been coined to describe the process of bidirectional learning. While a definition of Reciprocal Learning exists the boundary conditions of the concept are still ambiguous. In this paper, a framework for Reciprocal Learning in Human-Machine Collaboration for Industry 5.0 is presented to clarify what it is and what it isn’t. The approach is based on a multi-agent system perspective, comprising human and machine agents. Finally, an outlook on future research is presented.

Keywords

Schlüsselwörter

Reciprocal Learning, Work-Based Learning, Human-Machine Collaboration, Industry 5.0, Human-Machine Symbiosis

References

Referenzen

Leider ist der Eintrag nur auf Amerikanisches Englisch verfügbar.

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