The Effects of Agent Transparency on Human Interaction with an Autonomous Robotic Agent

Selkowitz, Anthony R.; Lakhmani, Shan; Chen, Jessie Y. C.; Boyce, Michael W.

doi:10.1177/1541931215591246

Cited by 29 publications

(31 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first theme we found was that transparency was associated with mixed findings. Indeed, studies find that higher transparency is related to increases in performance (e.g., Mercado et al, 2016), trust (Boyce et al, 2015), and perceived understanding of the autonomous agent (Harbers et al, 2011). Additionally, increasing agent transparency was associated with lower perceptions of time pressure and frustration (Wright et al, 2016).…”

Section: Independent Variablesmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Independent Variablesmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Of great interest to the HMT community was the reduction of workload found in the Benevolent condition. Findings from studies by Selkowitz et al (2015) and Mercado et al (2016) revealed that added transparency information did not mitigate workload. Transparency in previous work was provided within a SA model (see Barnes et al, 2017, for review) where visual displays were used to provide transparency into the system’s current state (Level 1), environmental constraints and logic (Level 2), and finally its projected state (Level 3).…”

Section: Discussionmentioning

confidence: 96%

“…Transparency is the intentional design of a system to communicate its capabilities and current state (Barnes et al, 2017; Lyons, 2013; Selkowitz, Lakhmani, Chen, & Boyce, 2015). It can be used to finely tune a human operator’s perception of the AT’s ability, intent, and situational constraints (Lyons, 2013).…”

Section: Supporting Affect Management With Transparencymentioning

confidence: 99%

Transparency in Autonomous Teammates

Panganiban

Matthews

Long

2019

Journal of Cognitive Engineering and Decision Making

View full text Add to dashboard Cite

Human–Machine teaming is a very near term standard for many occupational settings and still requires considerations for the design of autonomous teammates (ATs). Transparency of system processes is important for human–machine interaction and reliance but standards for its implementation are still being explored. Embedding social cues is a potential design approach, which may capture the social benefits of a team environment, yet vary with task setting. The current study examined the manipulation of transparency of benevolent intent from an AT within a piloting task requiring suppression of enemy defenses. Specifically, the benevolent AT maintained task communication as in a neutral condition, but included messages of support and awareness of errors. Benevolent communication reduced reported workload and increased reported team collaboration, indicating that this team intent was beneficial. In addition, trust and acceptance of the AT were rated higher by individuals tasked with depending on the system to protect them from missile threats. The need for information from ATs is beneficial, however may vary depending on team type.

show abstract

“…Other transparency definitions, across domains, describe transparency as the communication of information regarding the machine’s abilities (Mercado et al, 2016) and capabilities (Wohleber, Stowers, Chen, & Barnes, 2017). Transparency has been described as a process (Nedbal, Auinger, & Hochmeier, 2013), method (Lyons, Sadler, et al, 2017; Selkowitz, Lakhmani, Larios, & Chen, 2016), mechanism (Lyons, 2013; Theodorou, Wortham, & Bryson, 2017), property (Selkowitz, Lakhmani, Chen, & Boyce, 2015), or emergent characteristic (Ososky, Sanders, Jentsch, Hancock, & Chen, 2014) that provides information or explanations (Kim & Hinds, 2006) to a human operator to develop accurate mental models of the system. The type of information provided, known as information transparency (Bruni, Marquez, Brzezinski, Nehme, & Boussemart, 2007), includes what (Mark & Kobsa, 2005) the human operator or machine is doing (Lyons, 2013) and why a particular task is being conducted (Sanders, Wixon, Schafer, Chen, & Hancock, 2014).…”

Section: Introductionmentioning

confidence: 99%

Transparency: Transitioning From Human–Machine Systems to Human-Swarm Systems

Roundtree

Goodrich

Adams

2019

Journal of Cognitive Engineering and Decision Making

View full text Add to dashboard Cite

Swarm robotic systems are gaining in interest with the prospect of their use for various applications, including monitoring, tracking, infrastructure support, and protection. Prior human-swarm system research investigated transparency for these systems, but assumed perfect communication scenarios. Real-world human-swarm systems will not have perfect communication due to human processing limitations caused by a large number of individual entities, emergent behavior due to varying environments, and bandwidth issues. Factors that affect transparency or are influenced by transparency were identified from various human–machine system domains; however, understanding the relationship between factors can help designers identify what criteria can be implemented to achieve transparency for swarms. Challenges that may arise based on transparency criteria from human–machine systems are examined to identify improvements for spatial swarm systems.

show abstract

The Effects of Agent Transparency on Human Interaction with an Autonomous Robotic Agent

Cited by 29 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

Transparency in Autonomous Teammates

Transparency: Transitioning From Human–Machine Systems to Human-Swarm Systems

Contact Info

Product

Resources

About