In multi-member human-agent teams the communication and shared mental models within the team are essential for good teamwork and team performance. In some ways the mediating processes are even more important than in human-only team because the artificial agents of today lack many of the innate social behaviours that humans naturally possess. Research into human-agent teams have allowed designers of such teams to anticipate for complex interactions such as trust violation and repair scenarios. In this study a human-agent-agent team undertakes a search- and rescue mission with the human in a leading role, one of the agents free-roaming and the other agent under the human's direct control. Approximately one-third of the way through the mission, the autonomous agents initiated actions independently of human approval, thereby undermining operator trust. As a trust repair strategy the agent employs a promise to do better and a novel authority change by lowering its level of automation and presenting the option of restricting cooperation with the other agent.
We conducted the experiment with thirty participants divided into a two groups with differing trust repair strategies (promise only, promise with the authority change) and measured trust perception at three different time steps.
Results show no significant difference between the two trust repair strategies when directly comparing to trust. A positive correlation between the authority change trust repair strategy and task load on trust recovery was found. Through thematic analysis we did find that the shared mental model and communication richness to be dissonant to what participants expected which is in line with literature on the complexity of triadic teams.

Human-agent teamwork (HAT) is becoming increasingly prevalent in fields such as search and rescue (SAR), where effective collaboration between humans and artificial agents is crucial. Previous studies have shown that trust plays a pivotal role in the success of HATs, influencing decision-making, communication, and potentially overall team performance.

This research investigates the impact of agent-provided explanations about the agent's trust in humans (artificial trust) and corresponding behavior changes on human trust in the agent and their satisfaction with explanations during a simulated SAR task. Two types of explanations were explored: Trust-Explained (TE) explanations, where the agent explains its trust level and trust-based decisions, and Trust-Unexplained (TU) explanations, which solely describe the agent’s behavior without reference to trust dynamics. Besides, this research also investigates the correlation between human trust and explanation satisfaction, and in the end, whether the differences in the provided explanations result in differences in team performance and artificial trust.

The study involved 40 participants divided into two groups: an experimental group (the trust-enhanced explanation group) receiving TE explanations and a control group (the non-trust explanation group) receiving TU explanations. Participants' trust in the agent, satisfaction with the explanations, and team performance and artificial trust were measured and analyzed. Contrary to initial expectations, no statistically significant differences in explanation satisfaction and human trust in the agent were found between the two groups. However, a strong positive correlation was observed between participants' satisfaction with the explanations and their trust in the agent, indicating that explanation quality plays a crucial role in human trust development. Furthermore, no significant differences in team performance were detected, suggesting that trust explanations may not directly influence task outcomes. In the analysis of artificial trust, the agent in the trust-enhanced explanation group exhibited more conservative adjustments in trust levels compared to the non-trust explanation group. This conservative approach may have influenced players in the trust-enhanced explanation group to adopt a more cautious or deliberate decision-making process, potentially prioritizing the comprehension of explanations over the optimization of task performance.

For future research, it may be worth delving deeper into the influence of trust explanations on user behavior, the more complex HAT task environments, the relationship between artificial trust and user behavior, the dynamic and adaptive explanations, and the causal relationship between explanation satisfaction and human trust in the agent to understand further how trust can be fostered in HAT.

Mutual human-agent trust is of great importance for humans and agents to complete a task collaboratively. This paper aims at studying one of the factors influencing this mutual trust, the directability of humans to agents. Previous studies either take directability as a general concept without looking
into its different representations or fail to bridge the gap between directability and trust. This paper starts by analyzing directability’s different representations including commands, suggestions, and warnings, then investigates their influences on trust respectively. The experiment is set up in Block World For Teams(BW4T). Afterward, the trust is measured both by calculating the risk-taking behaviours by humans and using questionnaires. The result from the experiment suggests that collaborating with directability improves trust from humans to agents. Among different directability representations, commands and suggestions are the best ways to boost trust. However, the confounding factors
such as familiarity with the experiment also make a difference to the final result, those factors can be further investigated in the future.