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.
Highlights
With advances in automation, human cognitive modeling, applied artificial intelligence, and machine learning, human-machine teaming (HMT) can take many more forms than merely supervisory control by humans, or decision support by machines [1,2]
In the HMT ontology, the human(s) and CP elements are agents that are assigned to roles associated with tasks that are executed in real or simulated operational environments
This paper has presented an architectural framework to conceptualize, design, and evaluate adaptive human-machine teaming (HMT) options under a variety of what-if scenarios depicting alternate futures
Summary
Human cognitive modeling, applied artificial intelligence, and machine learning, human-machine teaming (HMT) can take many more forms than merely supervisory control by humans, or decision support by machines [1,2]. HMT can be adaptive with the human having the ability to intervene in machine operations at different levels to redirect resources, re-allocate tasks, modify workflow parameters, or adjust task sequences [3,4] This added complexity associated with adaptive HMT requires a formal modeling and analysis framework to explore and evaluate HMT options in terms of joint human-machine performance and safety in various simulated operational contexts [5]. The type of questions that such a framework can potentially address include determining the impact of dynamic task re-allocation on human attention, situation awareness, and cognitive load Answering such questions requires an experimentation testbed that employs a modeling, simulation, and analysis framework for systematic exploration of HMT options [6,7].
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