The role of intelligent agents becomes more social as they are expected to act in direct interaction, involvement and/or interdependency with humans and other artificial entities, as in Human-Agent Teams (HAT). The highly interdependent and dynamic nature of teamwork demands correctly calibrated trust among team members. Trust violations are an inevitable aspect of the cycle of trust and since repairing damaged trust proves to be more difficult than building trust initially, effective trust repair strategies are needed to ensure durable and successful team performance. The aim of this study was to explore the effectiveness of different trust repair strategies from an intelligent agent by measuring the development of human trust and advice taking in a Human-Agent Teaming task. Data for this study were obtained using a task environment resembling a first-person shooter game. Participants carried out a mission in collaboration with their artificial team member. A trust violation was provoked when the agent failed to detect an approaching enemy. After this, the agent offered one of four trust repair strategies, composed of the apology components explanation and expression of regret (either one alone, both or neither). Our results indicated that expressing regret was crucial for effective trust repair. After trust declined due to the violation by the agent, trust only significantly recovered when an expression of regret was included in the apology. This effect was stronger when an explanation was added. In this context, the intelligent agent was the most effective in its attempt of rebuilding trust when it provided an apology that was both affective, and informational. Finally, the implications of our findings for the design and study of Human-Agent trust repair are discussed.

a b s t r a c tDynamic Positioning (DP) is a computer-controlled process to automatically keep a floating vessel at a specific position or to follow a pre-defined path (tracking) by using its own propellers and thrusters. The human supervisory controller has no direct need to constantly know what the status is of all parts of the automation and the system it is controlling, because the highly automated DPS is controlling all components itself. Only after a failure arises, the operator needs to take over manual control and take appropriate action(s) to prevent the failure from harming the operation. As the supervisory controller may be out of the loop, swiftly taking over control may be problematic when failures arise. The purpose of the current study was to investigate whether automation of change detection enables human operators with low awareness of the automation and the system it is controlling to quickly recover awareness in emergency take-over situations. A 2 by 2 within subjects experiment was conducted using a DP simulation (n ¼ 22). Within-subjects factors were support (Yes, No) and interruption (Yes, No). Results showed that change detection support helps in the process of recovering situation awareness after it has been reduced, due to an interruption of the primary task of overseeing the automation. Interestingly, support was not beneficial to the participants in all conditions. In non-interrupted conditions the support unexpectedly resulted in higher workload, raising questions whether supervisory controllers should be supported continuously or only when it is required. Relevance to industry: The results show that change detection support has potential value in operational maritime environments, especially in situations where the DP operator has low situation awareness. Future research should investigate whether adaptive aiding could alleviate some of the negative effects of non-adaptive operator support in maritime environments.