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Skraaning and Jamieson raise some interesting issues related to the response of humans to automation failures and offer a taxonomy of failure types that broadens its definition. In this commentary a further attempt to broaden the scope of automation failures is made that places failures within a sociotechnical system of multiple humans and multiple machine components including automation. A suggestion of how one might understand the system’s response to automation failures is offered and the inclusion of autonomy is raised as another complication. 

In this extended abstract we present the design, development, and evaluation of a Minecraft-based simulated task environment to conduct human and AI teaming research. With the deluge of AI-driven applications and their infiltration into many activities of daily living, it is becoming necessary to look at ways that humans and AI can work together. There is a tremendous research burden associated with accurately evaluating the best practices and trade-offs when humans and AI have to collaborate together in completing critical tasks. Minecraft offers a low-cost alternative as an early investigating tool for researchers to build answers to emerging research questions before significantly investing in human-AI teaming activities in the real world. We demonstrate successfully via a simple rule-based AI, insights that could highly influence human-AI teaming activities can be derived to improve practical and viable development of protocols and procedures. Our findings indicate that simulated task environments play a critical role in furthering human AI teaming activities. 

Resilient teams overcome sudden, dynamic changes by enacting rapid, adaptive responses that maintain system effectiveness. We analyzed two experiments on human-autonomy teams (HATs) operating a simulated remotely piloted aircraft system (RPAS) and correlated dynamical measures of resilience with measures of team performance. Across both experiments, HATs experienced automation and autonomy failures, using a Wizard of Oz paradigm. Team performance was measured in multiple ways, using a mission-level performance score, a target processing efficiency score, a failure overcome score, and a ground truth resilience score. Novel dynamical systems metrics of resilience measured the timing of system reorganization in response to failures across RPAS layers, including vehicle, controls, communications layers, and the system overall. Time to achieve extreme values of reorganization and novelty of reorganization were consistently correlated with target processing efficiency and ground truth resilience across both studies. Correlations with mission-level performance and the overcome score were apparent but less consistent. Across both studies, teams displayed greater system reorganization during failures compared to routine task conditions. The second experiment revealed differential effects of team training focused on coordination coaching and trust calibration. These results inform the measurement and training of resilience in HATs using objective, real-time resilience analysis. 

ObjectiveThis study examines low-, medium-, and high-performing Human-Autonomy Teams’ (HATs’) communication strategies during various technological failures that impact routine communication strategies to adapt to the task environment. BackgroundTeams must adapt their communication strategies during dynamic tasks, where more successful teams make more substantial adaptations. Adaptations in communication strategies may explain how successful HATs overcome technological failures. Further, technological failures of variable severity may alter communication strategies of HATs at different performance levels in their attempts to overcome each failure. MethodHATs in a Remotely Piloted Aircraft System-Synthetic Task Environment (RPAS-STE), involving three team members, were tasked with photographing targets. Each triad had two randomly assigned participants in navigator and photographer roles, teaming with an experimenter who simulated an AI pilot in a Wizard of Oz paradigm. Teams encountered two different technological failures, automation and autonomy, where autonomy failures were more challenging to overcome. ResultsHigh-performing HATs calibrated their communication strategy to the complexity of the different failures better than medium- and low-performing teams. Further, HATs adjusted their communication strategies over time. Finally, only the most severe failures required teams to increase the efficiency of their communication. ConclusionHAT effectiveness under degraded conditions depends on the type of communication strategies enacted by the team. Previous findings from studies of all-human teams apply here; however, novel results suggest information requests are particularly important to HAT success during failures. ApplicationUnderstanding the communication strategies of HATs under degraded conditions can inform training protocols to help HATs overcome failures. 

Team workload is a team-level construct considered similar to, but not reducible to, individual workload and mediated by team coordination. Despite this, the conceptualization and measurement of team workload in action teams lags behind that of individual workload. In most empirical studies, team workload is often simply considered as the sum or average of individual team members’ workload. However, unique characteristics of action teams, such as interdependence and heterogeneity, suggest that traditional approaches to conceptualizing and measuring team workload may be inadequate or even misleading. As such, innovative approaches are required to accurately capture this complex construct. This paper presents the development of a simulation designed to investigate the influence of interdependence and demand levels on team workload measures within a 3-person action-team command and control scenario. Preliminary results, which suggest that our manipulations are effective, are provided and discussed. 

Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The workload of the ATCos can have negative effects on operational safety and airspace usage. To avoid overloading and ensure an acceptable workload level for the ATCos, it is important to predict the ATCos’ workload accurately for mitigation actions. In this paper, we first perform a review of research on ATCo workload, mostly from the air traffic perspective. Then, we briefly introduce the setup of the human-in-the-loop (HITL) simulations with retired ATCos, where the air traffic data and workload labels are obtained. The simulations are conducted under three Phoenix approach scenarios while the human ATCos are requested to self-evaluate their workload ratings (i.e., low-1 to high-7). Preliminary data analysis is conducted. Next, we propose a graph-based deep-learning framework with conformal prediction to identify the ATCo workload levels. The number of aircraft under the controller’s control varies both spatially and temporally, resulting in dynamically evolving graphs. The experiment results suggest that (a) besides the traffic density feature, the traffic conflict feature contributes to the workload prediction capabilities (i.e., minimum horizontal/vertical separation distance); (b) directly learning from the spatiotemporal graph layout of airspace with graph neural network can achieve higher prediction accuracy, compare to hand-crafted traffic complexity features; (c) conformal prediction is a valuable tool to further boost model prediction accuracy, resulting a range of predicted workload labels. The code used is available at Link. 

This expert panel is the first of a two-panel series marking the 40^thanniversary of “Cognitive Systems Engineering: New Wine in New Bottles” by Hollnagel and Woods (1983) and, arguably, the beginning of Cognitive Systems Engineering (CSE). These experts were there at (or near) the beginning, devising new methods, expanding and creating new theories, and revealing a new perspective on how complex systems sustain performance and fail. They also wrestled and struggled with these new ideas to propose and implement solutions to improve performance in a number of high-consequence industries. Whether in graduate school or as early-career professionals, they saw the surprises that served as signals that the thinking that brought us to that point would not, alone, be the thinking and doing that would take us further. They will each answer the question, “What ideas and perspectives are important about Cognitive Systems Engineering, and why?” 

Understanding how people trust autonomous systems is crucial to achieving better performance and safety in human-autonomy teaming. Trust in automation is a rich and complex process that has given rise to numerous measures and approaches aimed at comprehending and examining it. Although researchers have been developing models for understanding the dynamics of trust in automation for several decades, these models are primarily conceptual and often involve components that are difficult to measure. Mathematical models have emerged as powerful tools for gaining insightful knowledge about the dynamic processes of trust in automation. This paper provides an overview of various mathematical modeling approaches, their limitations, feasibility, and generalizability for trust dynamics in human-automation interaction contexts. Furthermore, this study proposes a novel and dynamic approach to model trust in automation, emphasizing the importance of incorporating different timescales into measurable components. Due to the complex nature of trust in automation, it is also suggested to combine machine learning and dynamic modeling approaches, as well as incorporating physiological data.