Human-Autonomy Teaming Using Flexible Human Performance Models: An Initial Pilot Study

Shannon, Christopher J.; Horney, David C.; Jackson, Kimberly F.; How, Jonathan P.

doi:10.1007/978-3-319-41959-6_18

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…As noted by Larson and DeChurch (2020, p. 10), “we are quickly approaching a time when digital technologies are as agentic as are human counterparts.” With continuous advancements in artificial intelligence (AI), autonomous agents can perform a greater number of dynamical functions in both teamwork and taskwork than ever before (Seeber et al, 2020), and they are beginning to be viewed as teammates rather than tools (Grimm et al, 2018a; Lyons et al, 2018). For example, autonomous agents can increasingly participate in teamwork activity involving coordination, task reallocation, and continuous interaction with humans and other autonomous agents (Chen et al, 2016; Johnson et al, 2012; Shannon et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…A number of frameworks have been proposed over several decades [19], [20], [21], [22] which focus on optimizing operator workload, attention and efficiency. Consequently, their focus has been on modeling the human [13], [14], [15]. [12] recently propose an interactive model of autonomy, where a system learns a model of its competence online.…”

Section: Related Workmentioning

confidence: 99%

“…For example, in manufacturing, where minimizing the economic cost of production is crucial, c rob could reflect the cost of operating a robot, while c hum and c demo could depend on the efficiency of a human collaborator. There exist a number of approaches [14], [15], [36] to model human performance. c f ail corresponds to the difficulty of fixing a mistake made by the robot.…”

Section: Cost Functionmentioning

confidence: 99%

“…In contrast, planners for task allocation [8], [9], [10] and adjusting the level of autonomy [11], [12] have been proposed for human-robot teams. The focus on these works is on handling spatiotemporal constraints and different human models [13], [14], [15]. However, they assume a static model of the robot's capabilities, which does not allow them to leverage robot learning in their framework.…”

Section: Introductionmentioning

confidence: 99%

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Synergistic Scheduling of Learning and Allocation of Tasks in Human-Robot Teams

Vats¹,

Kroemer²,

Likhachev³

2022

Preprint

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We consider the problem of completing a set of n tasks with a human-robot team using minimum effort. In many domains, teaching a robot to be fully autonomous can be counterproductive if there are finitely many tasks to be done. Rather, the optimal strategy is to weigh the cost of teaching a robot and its benefit-how many new tasks it allows the robot to solve autonomously. We formulate this as a planning problem where the goal is to decide what tasks the robot should do autonomously (act), what tasks should be delegated to a human (delegate) and what tasks the robot should be taught (learn) so as to complete all the given tasks with minimum effort. This planning problem results in a search tree that grows exponentially with n -making standard graph search algorithms intractable. We address this by converting the problem into a mixed integer program that can be solved efficiently using offthe-shelf solvers with bounds on solution quality. To predict the benefit of learning, we use an approximate simulation model of the tasks to train a precondition model that is parameterized by the training task. Finally, we evaluate our approach on peg insertion and Lego stacking tasks-both in simulation and realworld, showing substantial savings in human effort.

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Modeling, simulation, and trade‐off analysis for multirobot, multioperator surveillance

Humann

Fletcher

Gerdes

2023

Systems Engineering

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As unmanned vehicles become smaller and more autonomous, it is becoming feasible to use them in large groups with comparatively few human operators. Design and analysis of such distributed systems are complicated by the many interactions among agents and phenomena of human behavior. In particular, human susceptibility to fatigue and cognitive overload can introduce errors and uncertainty into the system. In this paper, we demonstrate how advanced computational tools can help to overcome these engineering difficulties by optimizing multirobot, multioperator surveillance systems for cost, speed, accuracy, and stealth according to diverse user preferences in multiple case studies. The tool developed is a graphical user interface that returns the optimal number and mix of diverse agent types as a function of the user's trade‐off preferences. System performance prediction relies on a multiagent simulation with submodels for human operators, fixed‐wing unmanned aerial vehicles (UAVs), quadrotor UAVs, and flapping wing UAVs combined in different numbers.

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Human-Autonomy Teaming Using Flexible Human Performance Models: An Initial Pilot Study

Cited by 8 publications

References 13 publications

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

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

Synergistic Scheduling of Learning and Allocation of Tasks in Human-Robot Teams

Modeling, simulation, and trade‐off analysis for multirobot, multioperator surveillance

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