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Systems 2018, 6(4), 44; https://doi.org/10.3390/systems6040044

Architectural Framework for Exploring Adaptive Human-Machine Teaming Options in Simulated Dynamic Environments

Intelligent Systems Technology, Inc., Los Angeles, CA 90066, USA
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Received: 1 November 2018 / Revised: 6 December 2018 / Accepted: 10 December 2018 / Published: 13 December 2018
(This article belongs to the Special Issue Model-Based Systems Engineering)
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Abstract

With the growing complexity of environments in which systems are expected to operate, adaptive human-machine teaming (HMT) has emerged as a key area of research. While human teams have been extensively studied in the psychological and training literature, and agent teams have been investigated in the artificial intelligence research community, the commitment to research in HMT is relatively new and fueled by several technological advances such as electrophysiological sensors, cognitive modeling, machine learning, and adaptive/adaptable human-machine systems. This paper presents an architectural framework for investigating HMT options in various simulated operational contexts including responding to systemic failures and external disruptions. The paper specifically discusses new and novel roles for machines made possible by new technology and offers key insights into adaptive human-machine teams. Landed aircraft perimeter security is used as an illustrative example of an adaptive cyber-physical-human system (CPHS). This example is used to illuminate the use of the HMT framework in identifying the different human and machine roles involved in this scenario. The framework is domain-independent and can be applied to both defense and civilian adaptive HMT. The paper concludes with recommendations for advancing the state-of-the-art in HMT. View Full-Text
Keywords: human-machine teaming; human-machine teams; architectural framework; dynamic function allocation; intent inferencing; machine learning human-machine teaming; human-machine teams; architectural framework; dynamic function allocation; intent inferencing; machine learning
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Madni, A.M.; Madni, C.C. Architectural Framework for Exploring Adaptive Human-Machine Teaming Options in Simulated Dynamic Environments. Systems 2018, 6, 44.

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