Trust and Wellbeing in Transparent Human-Robot Interaction Teams

This PhD project aims at the design of a novel AI cognitive architecture for transparent collaboration amongst a group of human participants and cobots. In particular. it focuses on designing an AI explanatory architecture to enhance trust, wellbeing and harmony in heterogenous collaborative teams. The cognitive architecture will be able to read the intention of the two agents and provide explanatory strategies and information for trustworthy interaction to enhance the harmony and social dynamics of the interaction and group performance.

The main novelty contribution, and impact potential, relies in the consideration of third-party users (e.g., observer/co-worker of a human-cobot joint assembly task) and the role of AI explanatory strategies in supporting such triadic interaction and the quality and harmony of the interaction. The cognitive architecture will initially build on Vinanzi et als (2019, 2020) computational modelling approach for intention reading, further integrating the latest machine learning methodologies for learning-based, dynamic cooperation.

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