As intelligent agents (embodied or otherwise) become more sophisticated, transparency has become a hot topic in recent years and has been identified by various government agencies (e.g., guidelines...

We examined the effects of communication style on human performance, workload, situation awareness, and trust in a robot in a human-robot team. In a 2 × 2 mixed factor study, participants were teamed with a simulated robot to conduct a cordon-and-search style task. Participants were assigned to a communication style (Directive vs. Non-directive; between subjects), and both groups experienced varied periods of task load (high vs. low task load; within-subjects). Results indicate that task load influenced the participants' task performance more than communication style. However, there were some differential effects on response time and workload due to communication style. Participants in the Non-directive group did not report a higher workload than those in the Directive condition, even though objective measures of workload (i.e., eye-tracking measures) indicated they experienced a higher workload. These results may be due to the presence of feedback inherent in the differing communication styles.

The eight papers in this special issue focus on agent and system transparency in human-machine systems. The concept of transparency is investigated in a variety of contexts of human agent interaction—from a single robot to multiple heterogeneous agents and swarms. Two studies examine the effects of individual and cultural differences and, based on the compelling results, provide design recommendations related to transparent interfaces. One of the papers provides a thorough review on theoretical aspects of agent transparency and empirical findings on operator performance, situation awareness, trust and workload, among other outcomes. These measures are also among some of the most common metrics reported in studies in this special issue.

The primary purpose of this article is to determine the impact of a simulated agent's transparency on human performance and related variables, such as response time, workload, and trust calibration. The agent supports participants as they complete a base defense task by managing a team of heterogeneous unmanned vehicles and serves as a decision aid to the human. Three conditions of transparency are explored. In condition 1, the agent displays only the basic information (map of vehicle location and proposed routes). In condition 2, the agent displays the basic information and an explanation of its reasoning. In condition 3, the agent displays the basic information, reasoning, and uncertainties involved in the plans. Results show that participants exhibit better performance and trust calibration in the high-transparency conditions without perceiving a significant increase in workload. However, response time also increased, likely due to the additional processing time needed for conditions with more information. Overall, our findings indicate that the increased agent transparency can improve human-agent decision making and performance, but with a small cost of the efficiency (timeliness) of task completion.

Agent transparency is an important contributor to human performance, situation awareness (SA), and trust in human-agent teaming. However, agent transparency's effects on human performance when the agent is unreliable have yet to be examined. This paper examined how the transparency and reliability of an autonomous robotic squad member (ASM) affected a human observer's task performance, workload, SA, trust in the robot, and perceptions of the robot. In a 2 (ASM transparency) × 2 (ASM reliability) within-subject design experiment, participants monitored a simulated soldier squad that included an ASM as it traversed a simulated training environment, while concurrently monitoring the environment for targets. There was no difference in participants' performance on the target detection task, workload, or SA due to either ASM transparency or reliability. ASM reliability influenced participant trust and perceptions of the robot. Results suggest that reliability may be a stronger influence on the human's perceptions of the robot than transparency. Robot errors had a profound and lasting effect on the participants' perception of the robot's future reliability and resulted in reduced confidence in their assessments of the robot's reliability. These findings could have important implications for the continued use of automated systems when the user is aware of system errors.

Published with license by Taylor & Francis Group, LLC. This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address. A perspective oriented to humane and social values is adopted, formulating the challenges in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Seven Grand Challenges are identified and presented in this article: Human-Technology Symbiosis; Human-Environment Interactions; Ethics, Privacy and Security; Well-being, Health and Eudaimonia; Accessibility and Universal Access; Learning and Creativity; and Social Organization and Democracy. Although not exhaustive, they summarize the views and research priorities of an international interdisciplinary group of experts, reflecting different scientific perspectives, methodological approaches and application domains. Each identified Grand Challenge is analyzed in terms of: concept and problem definition; main research issues involved and state of the art; and associated emerging requirements. BACKGROUND This article presents the results of the collective effort of a group of 32 experts involved in the community of the Human Computer Interaction International (HCII) Conference series. The group’s collaboration started in early 2018 with the collection of opinions from all group members, each asked to independently list and describe five HCI grand challenges. During a one-day meeting held on the 20th July 2018 in the context of the HCI International 2018 Conference in Las Vegas, USA, the identified topics were debated and challenges were formulated in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Further analysis and consolidation led to a set of seven Grand Challenges presented herein. This activity was organized and supported by the HCII Conference series.

Communication of the inner workings of an intelligent agent, or agent transparency, is important for human operators' calibration of their trust in the agent. Recent work has focused on methods of communicating this information in the context of agent-assisted mission planning. This investigation looked at the framing of agent communication and how it might interact with level of agent transparency. Results showed that critical framing improved operator's perception and trust of the agent regardless of the agent's transparency level. Further, findings suggest that the effects of framing are especially pronounced in low transparency applications.

Two human factors studies were conducted to assess the effectiveness of intelligent agents' user interfaces that were designed based on the Situation awareness-based Agent Transparency (SAT) model. Results show that agents' transparency (based on the SAT model) can benefit operator performance and support proper calibration of trust in the agents. Increasing levels of transparency enhanced operator's perceived trust in the agents, but only to a degree. When uncertainty was added to the interface, operator's trust did not further increase. Finally, the subjective workload data suggest that the benefits of increasing agent transparency do not have to be associated with higher levels of operator workload.

The purpose of the paper is to discuss human centered design implications for shared decision making between humans and autonomous systems in complex environments. Design implications are generated based on empirical results from two research paradigms. In the first paradigm, an intelligent agent (RoboLeader) supervised multiple subordinate systems and was in turn supervised by the human operator. The Robo Leader research varied number of subordinate units, task difficulty, agent reliability, type of agent errors, and partial autonomy. The second paradigm involved human interaction with partially and fully autonomous systems. Design implications from both paradigms are evaluated -- relating to multitasking, adaptive systems, false alarms, individual differences, operator trust, and allocation of human and agent tasks for partial autonomy. We conclude that mixed-initiative decision sharing depends on designing interfaces that support human-agent transparency.

We discuss the Situation awareness-based Agent Transparency (SAT) model and its three levels of information to support agent transparency and operator situation awareness of the agent: agent's current action or plan, agent's reasoning information, and agent's projections of future outcomes. Two studies -- Autonomous Squad Member and IMPACT -- are summarized to illustrate the utility of agent transparency for the overall human-agent team performance.

The human factors literature on intelligent systems was reviewed in relation to the following: efficient human supervision of multiple robots, appropriate human trust in the automated systems, maintenance of human operator's situation awareness, individual differences in human-agent (H-A) interaction, and retention of human decision authority. A number of approaches-from flexible automation to autonomous agents-were reviewed, and their advantages and disadvantages were discussed. In addition, two key human performance issues (trust and situation awareness) related to H-A teaming for multirobot control and some promising user interface design solutions to address these issues were discussed. Some major individual differences factors (operator spatial ability, attentional control ability, and gaming experience) were identified that may impact H-A teaming in the context of robotics control.

In all human-robot interaction, trust is an important element to consider because the presence or absence of trust certainly impacts the ultimate outcome of that interaction. Limited research exists that delineates the development and maintenance of this trust in various operational contexts. Our own prior research has investigated theoretical and empirically supported antecedents of human-robot trust. Here, we describe progress to date relating to the development of a comprehensive human-robot trust model based on our ongoing program of research.

A military multitasking environment was simulated to examine the effects of unreliable automation on the performance of robotics operators. The main task was to manage a team of four ground robots with the assistance of RoboLeader, an intelligent agent capable of coordinating the robots and changing their routes based upon developments in the mission environment. RoboLeader's recommendations were manipulated to be either false-alarm prone or miss prone, with a reliability level of either 60% or 90%. The visual density of the targeting environment was manipulated by the presence or absence of friendly soldiers. Results showed that the type of RoboLeader unreliability (false-alarm vs. miss prone) affected operator's performance of tasks involving visual scanning (target detection, route editing, and situation awareness). There was a consistent effect of visual density for multiple performance measures. Participants with higher spatial ability performed better on the two tasks that required the most visual scanning (target detection and route editing). Participants' attentional control impacted their overall multitasking performance, especially during their execution of the secondary tasks (communication and gauge monitoring).

The purpose of this paper is to review research pertaining to the limitations and advantages of supervisory control for unmanned systems. We identify and discuss results showing technologies that mitigate the observed problems such as specialized interfaces, and adaptive systems. In the report, we first present an overview of definitions and important terms of supervisory control and human-agent teaming. We then discuss human performance issues in supervisory control of multiple robots with regard to operator multitasking performance, trust in automation, situation awareness, and operator workload. In the following sections, we review research findings for specific areas of supervisory control of multiple ground robots, aerial robots, and heterogeneous robots (using different types of robots in the same mission). In the last section, we review innovative techniques and technologies designed to enhance operator performance and reduce potential performance degradations identified in the literature.

We simulated a military reconnaissance environment and examined the performance of ground robotics operators who were instructed to utilize streaming video from an unmanned aerial vehicle to navigate his/her ground robot to the locations of the targets. We evaluated participants' spatial ability and examined if it affected their performance or perceived workload. Results showed that participants with higher spatial ability performed significantly better in target-mapping performance and reported less workload than those with lower spatial ability. Participants with poor sense-of-direction performed significantly worse in the target search task in the night condition compared with those with better sense-of-direction.

We developed an intelligent agent, RoboLeader, that could assist human operators in route planning for a team of ground robots. We compared the operators' target detection performance in the 4-robot and 8-robot conditions. Results showed that the participants detected significantly less targets and had significantly worse situation awareness when there were 8 robots compared to the 4-robot condition. Those participants with higher spatial ability detected more targets than did those with lower spatial ability. Participants' self-assessed workload was affected by the number of robots under control, their gender, and their attentional control ability.

This study investigated the performance and workload of the combined position of gunner and robotics operator in a simulated military multitasking environment. Specifically, we investigated how aided target recognition (AiTR) capabilities for the gunnery task with imperfect reliability (false-alarm-prone vs. miss-prone) might affect the concurrent robotics and communication tasks. Additionally, we examined whether performance was affected by individual differences in spatial ability and attentional control. Results showed that when the robotics task was simply monitoring the video, participants had the best performance in their gunnery and communication tasks and the lowest perceived workload, compared with the other robotics tasking conditions. There was a strong interaction between the type of AiTR unreliability and participants' perceived attentional control. Overall, for participants with higher perceived attentional control, false-alarm-prone alerts were more detrimental; for low attentional control participants, conversely, miss-prone automation was more harmful. Low spatial ability participants preferred visual cueing, and high spatial ability participants favored tactile cueing. Potential applications of the findings include personnel selection for robotics operation, robotics user interface designs, and training development.

We simulated a military mounted environment and examined the performance of the combined position of gunner and robotics operator and how aided target recognition (AiTR) capabilities (delivered either through tactile or tactile + visual cueing) for the gunnery task might benefit the concurrent robotics and communication tasks. Results showed that participants' teleoperation task improved significantly when the AiTR was available to assist them with their gunnery task. However, the same improvement was not found for semi-autonomous robotics task performance. Additionally, when teleoperating, those participants with higher spatial ability outperformed those with lower spatial ability. However, performance gap between those with higher and lower spatial ability appeared to be narrower when the AiTR was available to assist the gunnery task. Participants' communication task performance also improved significantly when the gunnery task was aided by AiTR. Finally, participant's perceived workload was significantly higher when they teleoperated a robotic asset and when their gunnery task was unassisted.

In the future, it will become more common for humans to team up with robotic systems to perform tasks that humans cannot realistically accomplish alone. Even for autonomous and semiautonomous systems, teleoperation will be an important default mode. However, teleoperation can be a challenging task because the operator is remotely located. As a result, the operator's situation awareness of the remote environment can be compromised and the mission effectiveness can suffer. This paper presents a detailed examination of more than 150 papers covering human performance issues and suggested mitigation solutions. The paper summarizes the performance decrements caused by video images bandwidth, time lags, frame rates, lack of proprioception, frame of reference, two-dimensional views, attention switches, and motion effects. Suggested solutions and their limitations include stereoscopic displays, synthetic overlay, multimodal interfaces, and various predicative and decision support systems.

In this paper, we conducted a comprehensive survey of the effects of different frame rates (FRs) on human performance and reviewed more than 50 studies and summarized them in the areas of psychomotor performance, perceptual performance, behavioral effects, and subjective perception. Overall, there seems to be strong support for a threshold of around 15 Hz for many tasks, including those that are psychomotor and perceptual in nature. Less impressive yet acceptable performance may be accomplished at around 10 Hz for many tasks. Subjective reactions to the quality and watchability of videos seem to support rates of 5 Hz, although videos presented at 15 Hz and above are generally more widely preferred. These generalizations regarding superior and acceptable FRs may also be subject to the effects of several moderating factors such as display characteristics, nature of the tasks, viewing condition, additional cues, and user experience.