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Article

Engineering Human–Machine Teams for Trusted Collaboration

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Simulation Science Center Clausthal-Göttingen, Technische Universität Clausthal, 38678 Clausthal-Zellerfeld, Germany
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Institute for Software and Systems Engineering, Technische Universität Clausthal, 38678 Clausthal-Zellerfeld, Germany
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School of Economics and Social Sciences, Helmut-Schmidt-Universität Hamburg, 22043 Hamburg, Germany
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Department of Informatics, Technische Universität Clausthal, 38678 Clausthal-Zellerfeld, Germany
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Institute of Computer Science and Campus Institute Data Science, Georg-August-Universität Göttingen, 37077 Göttingen, Germany
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Institute for Electrical Information Technology, Technische Universität Clausthal, 38678 Clausthal-Zellerfeld, Germany
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Institute of Management and Economics, Technische Universität Clausthal, 38678 Clausthal-Zellerfeld, Germany
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Institute of Mathematics, Technische Universität Clausthal, 38678 Clausthal-Zellerfeld, Germany
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Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2020, 4(4), 35; https://doi.org/10.3390/bdcc4040035
Received: 9 October 2020 / Revised: 6 November 2020 / Accepted: 18 November 2020 / Published: 23 November 2020
The way humans and artificially intelligent machines interact is undergoing a dramatic change. This change becomes particularly apparent in domains where humans and machines collaboratively work on joint tasks or objects in teams, such as in industrial assembly or disassembly processes. While there is intensive research work on human–machine collaboration in different research disciplines, systematic and interdisciplinary approaches towards engineering systems that consist of or comprise human–machine teams are still rare. In this paper, we review and analyze the state of the art, and derive and discuss core requirements and concepts by means of an illustrating scenario. In terms of methods, we focus on how reciprocal trust between humans and intelligent machines is defined, built, measured, and maintained from a systems engineering and planning perspective in literature. Based on our analysis, we propose and outline three important areas of future research on engineering and operating human–machine teams for trusted collaboration. For each area, we describe exemplary research opportunities. View Full-Text
Keywords: human–machine collaboration; human–machine teams; human-in-the-loop; trust within teams; sensor and data analysis technologies human–machine collaboration; human–machine teams; human-in-the-loop; trust within teams; sensor and data analysis technologies
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MDPI and ACS Style

Alhaji, B.; Beecken, J.; Ehlers, R.; Gertheiss, J.; Merz, F.; Müller, J.P.; Prilla, M.; Rausch, A.; Reinhardt, A.; Reinhardt, D.; Rembe, C.; Rohweder, N.-O.; Schwindt, C.; Westphal, S.; Zimmermann, J. Engineering Human–Machine Teams for Trusted Collaboration. Big Data Cogn. Comput. 2020, 4, 35. https://doi.org/10.3390/bdcc4040035

AMA Style

Alhaji B, Beecken J, Ehlers R, Gertheiss J, Merz F, Müller JP, Prilla M, Rausch A, Reinhardt A, Reinhardt D, Rembe C, Rohweder N-O, Schwindt C, Westphal S, Zimmermann J. Engineering Human–Machine Teams for Trusted Collaboration. Big Data and Cognitive Computing. 2020; 4(4):35. https://doi.org/10.3390/bdcc4040035

Chicago/Turabian Style

Alhaji, Basel, Janine Beecken, Rüdiger Ehlers, Jan Gertheiss, Felix Merz, Jörg P. Müller, Michael Prilla, Andreas Rausch, Andreas Reinhardt, Delphine Reinhardt, Christian Rembe, Niels-Ole Rohweder, Christoph Schwindt, Stephan Westphal, and Jürgen Zimmermann. 2020. "Engineering Human–Machine Teams for Trusted Collaboration" Big Data and Cognitive Computing 4, no. 4: 35. https://doi.org/10.3390/bdcc4040035

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