Engineering Human–Machine Teams for Trusted Collaboration release_uoanpxph5fbglo7sl3pytbkykm

by Basel Alhaji, Janine Beecken, Rüdiger Ehlers, Jan Gertheiss, Felix Merz, Jörg P. Müller, Michael Prilla, Andreas Rausch, Andreas Reinhardt, Delphine Reinhardt, Christian Rembe, Niels-Ole Rohweder (+3 others)

Published in Big Data and Cognitive Computing by MDPI AG.

2020   p35

Abstract

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.
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Date   2020-11-23
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