Adapting Behaviour Based On Trust In Human-Agent Ad Hoc Teamwork
This work addresses the challenge of improving team performance in human-agent collaborations by adapting to trust dynamics, though it appears incremental as it builds on existing trust and teamwork concepts.
The authors tackled the problem of enabling an agent to infer human trust levels and adapt its behavior accordingly in ad hoc teamwork scenarios, resulting in a framework that was validated in a real-world setting to analyze its impact on trust.
This work proposes a framework that incorporates trust in an ad hoc teamwork scenario with human-agent teams, where an agent must collaborate with a human to perform a task. During the task, the agent must infer, through interactions and observations, how much the human trusts it and adapt its behaviour to maximize the team's performance. To achieve this, we propose collecting data from human participants in experiments to define different settings (based on trust levels) and learning optimal policies for each of them. Then, we create a module to infer the current setting (depending on the amount of trust). Finally, we validate this framework in a real-world scenario and analyse how this adaptable behaviour affects trust.