To the aim of constructing effective human-AI teams, that can be useful for improving caregiving in medicine and enhancing human performance also in other sectors (i.e., teaching), agents which interact with humans should be endowed with an emotion recognition and management module, capable of empathy, and of modeling aspects of the Theory of Mind, in the sense of being able to reconstruct what someone is thinking or feeling. In this paper, we propose an architecture for such a module, based upon an enhanced notion of Behavior Trees. We illustrate the effectiveness of the proposed architecture on a significant example and on a wider case study.

We propose a novel architecture for agents to be employed in Human-AI Teaming in various, even critical, domains. The architecture is based on affective computing, empathy, and Theory of Mind (ToM) and uses a description of the user profile and the operational, professional, and ethical requirements of the domain in which the agent operates. Moreover, it encompasses: a Knowledge Graph (KG), which is aimed at representing the various kinds of knowledge of the scenario at hand; a Behavior Tree (BT) enhanced with empathy-aware capabilities; a neural component, which elaborates sensor input from devices that monitor the user and input from the KG. The enhanced BT is the component that interacts with the user, making actions or providing suggestions. It also returns feedback to the KG to update it based on user experience. This two-way interaction between KG and BT is a major novelty of our work. As further contributions, we showcase an application of the proposed architecture on a driver co-pilot scenario and provide an early stage implementation of our architecture, mainly based on a Prolog implementation of the BT component.

In the near future, transportation systems will include both autonomous vehicles and human-operated vehicles sharing the same traffic conditions. Human drivers will have difficulty predicting the actions of autonomous vehicles and the latter will face challenges due to complex decision-making algorithms and dynamic environments. The lack of standardized interaction protocols between autonomous vehicles and human drivers further complicates safe decision-making. This paper proposes an AI-based advisory framework to enhance human driving skills in mixed autonomy traffic and improve autonomous vehicles in a Human–AI teaming fashion. Our framework is composed of both a centralized component and a decentralized component. The centralized component primarily identifies driving style and trajectory parameters that impact traffic efficiency across large-scale traffic networks shared by human drivers and autonomous vehicles. At the local level, however, our proposed framework features a decentralized, agent-based strategy to enable effective coordination between human and autonomous vehicles—especially at complex intersections. An initial prototype is modeled and implemented in a desktop virtual reality environment for testing and training.