Effective Team Interaction for Adaptive Training and Situation Awareness in Human-Autonomy Teaming

Demir, Mustafa; McNeese, Nathan J.; Johnson, Craig J.; Gorman, Jamie C.; Grimm, David; Cooke, Nancy J.

doi:10.1109/cogsima.2019.8724202

Cited by 14 publications

(11 citation statements)

References 18 publications

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“…Therefore, if one or more autonomous agents are included as team members, many of the same known effective team design principles should be employed (e.g., interdependence, role clarity, norm development, expertise variety, feedback; for example, Campion et al [1996]; Morgeson et al [2006]). Moreover, we also point to research that indicates that humans do perceive autonomous agents as team members if they have certain attributes (e.g., Lyons et al, 2018), and that the team task can be designed carefully to elicit complex teamwork and communication involving humans and autonomous agents (Demir et al, 2019; McNeese et al, 2018). And as autonomous agents increase in AI, they may become more and more agentic, proactive, and synchronized with humans.…”

Section: Discussionmentioning

confidence: 79%

“…In the 1990s, as the above quotes illustrate, discussions emerged with respect to autonomous agents playing roles as genuine team players. Therefore, the concept of HAT has been considered by academics for three decades, but it was not until more recently that the term HAT has emerged and been used frequently (e.g., Demir et al, 2019; Demir, Likens, et al, 2018; Demir, McNeese, et al 2018; Dubey et al, 2020, Fiore & Wiltshire, 2016, Grimm et al, 2018a, 2018b, Grimm et al, 2018c; McNeese et al, 2018, 2019; Shannon et al, 2017; Wohleber et al, 2017). We believe the emerging use of the term HAT is due to significant advances in AI, machine learning, and cognitive modeling.…”

Section: Introductionmentioning

confidence: 99%

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Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

et al. 2020

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Objective We define human–autonomy teaming and offer a synthesis of the existing empirical research on the topic. Specifically, we identify the research environments, dependent variables, themes representing the key findings, and critical future research directions. Background Whereas a burgeoning literature on high-performance teamwork identifies the factors critical to success, much less is known about how human–autonomy teams (HATs) achieve success. Human–autonomy teamwork involves humans working interdependently toward a common goal along with autonomous agents. Autonomous agents involve a degree of self-government and self-directed behavior (agency), and autonomous agents take on a unique role or set of tasks and work interdependently with human team members to achieve a shared objective. Method We searched the literature on human–autonomy teaming. To meet our criteria for inclusion, the paper needed to involve empirical research and meet our definition of human–autonomy teaming. We found 76 articles that met our criteria for inclusion. Results We report on research environments and we find that the key independent variables involve autonomous agent characteristics, team composition, task characteristics, human individual differences, training, and communication. We identify themes for each of these and discuss the future research needs. Conclusion There are areas where research findings are clear and consistent, but there are many opportunities for future research. Particularly important will be research that identifies mechanisms linking team input to team output variables.

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Section: Discussionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

et al. 2020

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“…However, most recently, HAT has been frequently used in association with highly intelligent agents based on AI, machine learning, and cognitive modeling that are as competent as humans. Such work has directed the implementation of human-autonomy collaboration from concept to practice [25][26][27][28][29].…”

Section: Human Autonomy Teamwork (Hat)mentioning

confidence: 99%

Novel Human-in-the-Loop (HIL) Simulation Method to Study Synthetic Agents and Standardize Human–Machine Teams (HMT)

Damacharla¹,

Dhakal

Bandreddi

et al. 2020

Applied Sciences

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This work presents a multi-year study conducted at the University of Toledo, aimed at improving human–machine teaming (HMT) methods and technologies. With the advent of artificial intelligence (AI) in 21st-century machines, collaboration between humans and machines has become highly complicated for real-time applications. The penetration of intelligent and synthetic assistants (IA/SA) in virtually every field has opened up a path to the area of HMT. When it comes to crucial tasks such as patient treatment/care, industrial production, and defense, the use of non-standardized HMT technologies may pose a risk to human lives and cost billions of taxpayer dollars. A thorough literature survey revealed that there are not many established standards or benchmarks for HMT. In this paper, we propose a method to design an HMT based on a generalized architecture. This design includes the development of an intelligent collaborative system and the human team. Followed by the identification of processes and metrics to test and validate the proposed model, we present a novel human-in-the-loop (HIL) simulation method. The effectiveness of this method is demonstrated using two controlled HMT scenarios: Emergency care provider (ECP) training and patient treatment by an experienced medic. Both scenarios include humans processing visual data and performing actions that represent real-world applications while responding to a Voice-Based Synthetic Assistant (VBSA) as a collaborator that keeps track of actions. The impact of various machines, humans, and HMT parameters is presented from the perspective of performance, rules, roles, and operational limitations. The proposed HIL method was found to assist in standardization studies in the pursuit of HMT benchmarking for critical applications. Finally, we present guidelines for designing and benchmarking HMTs based on the case studies’ results analysis.

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“…However, most recently, HAT has been frequently used in association with highly intelligent agents based on AI, machine learning, and cognitive modeling that are as competent as humans. And such work has directed the implementation of human-autonomy collaboration from concept to practice [25][26][27][28][29].…”

Section: Human Autonomy Teamwork (Hat)mentioning

confidence: 99%

Novel Human-in-the-Loop (HIL) Simulation Method to Study Synthetic Agents and Standardize Human-Machine Teams (HMT)

Damacharla¹,

Dhakal²,

Bandreddi³

et al. 2020

Preprint

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This paper presents a multi-year research study conducted at the University of Toledo, aimed at improving human-machine teaming (HMT) methods and technologies. With the incorporation of artificial intelligence (AI) into 21st-century machines, collaboration between humans and machines has become highly complicated for real-time applications. The penetration of intelligent and synthetic assistants (IA/SA) in virtually every field has opened a path for the new area of HMT. When it comes to crucial tasks such as patient treatment and care, defense, and industrial production, the use of non-standardized HMT technologies may pose a risk to human lives as well as up to billions of taxpayer dollars. A thorough literature survey revealed that there are not many established standards or benchmarks for HMT. In this paper, we aim to address the lack of HMT design and development standards. We propose a method to design an HMT based on a generalized architecture. This design includes the development of an intelligent collaborative system and human team. Followed by processes and metrics to test and validate the proposed model, we present a novel human-in-the-loop (HIL) simulation method using two controlled HMT scenarios; first, emergency care provider (ECP) training, and second, patient treatment by an experienced medic. Both scenarios include humans processing visual data and performing actions that represent real-world applications while responding to a Voice-Based Synthetic Assistant (VBSA) as a collaborator that keeps track of actions. As part of these HMT simulation studies, the impact of each parameter related to machines, humans, and HMT (such as errors made by humans and by the VBSA) is presented from the perspective of performance, rules, roles, and operation limitations. In addition, based on the analysis of results from two case studies, we presented guidelines for designing future HMT. Our conclusions indicate that the proposed HIL can be used to perform standardization studies in the pursuit of developing techniques for benchmarking HMT that can be used in critical situations.

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Effective Team Interaction for Adaptive Training and Situation Awareness in Human-Autonomy Teaming

Cited by 14 publications

References 18 publications

Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

Novel Human-in-the-Loop (HIL) Simulation Method to Study Synthetic Agents and Standardize Human–Machine Teams (HMT)

Novel Human-in-the-Loop (HIL) Simulation Method to Study Synthetic Agents and Standardize Human-Machine Teams (HMT)

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