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.

Distributed Ledgers, such as Blockchain and the IOTA Tangle, offer secure, transparent, and decentralized solutions that can streamline decision-making processes in agriculture. IOTA, in particular, stands out as a fee-less and scalable architecture designed to support real-time data transmission from Internet-of-Things sensors. This makes it a promising candidate for enhancing agricultural monitoring and management. This study systematically evaluates IOTA’s potential to meet the specific needs of agriculture using the Protocol Search Appraisal Synthesis Analysis Report (PSALSAR) methodology. The IOTA Tangle provides an efficient framework for precision farming by enabling seamless data exchange without the high transaction costs typical of traditional blockchain technologies. The research also highlights how this Distributed Ledger Technologies can enhance the monitoring of environmental factors, optimize resource usage, improve traceability of the production chain and ultimately contribute to more sustainable farming practices.

Abstract Autonomous robots can be employed in exploring unknown environments and performing many tasks, such as, e.g. detecting areas of interest, collecting target objects, etc. Deep reinforcement learning (RL) is often used to train this kind of robot. However, concerning the artificial environments aimed at testing the robot, there is a lack of available data sets and a long time is needed to create them from scratch. A good data set is in fact usually produced with high effort in terms of cost and human work to satisfy the constraints imposed by the expected results. In the first part of this paper, we focus on the specification of the properties of the solutions needed to build a data set, making the case of environment exploration. In the proposed approach, rather than using imperative programming, we explore the possibility of generating data sets using constraint programming in Prolog. In this phase, geometric predicates describe a virtual environment according to inter-space requirements. The second part of the paper is focused on testing the generated data set in an AI gym via space search techniques. We developed a Neuro-Symbolic agent built from the following: (i) A deep Q-learning component implemented in Python, able to address via RL a search problem in the virtual space; the agent has the goal to explore a generated virtual environment to seek for a target, improving its performance through a RL process. (ii) A symbolic component able to re-address the search when the Q-learning component gets stuck in a part of the virtual environment; these components stimulate the agent to move to and explore other parts of the environment. Wide experimentation has been performed, with promising results, and is reported, to demonstrate the effectiveness of the approach.