
Holistic framework for AI in critical network infrastructures
This document establishes the main foundations of the AI4REALNET project, in particular, the following key outcomes: - The formal specification of domain-specific use cases (UCs), replicating real-world operating scenarios involving human operator

Position paper on AI for the operation of critical energy and mobility network infrastructures
This position paper outlines AI4REALNET’s approach to applying AI in network infrastructure operations, translating application needs into algorithmic proposals for effective human-AI collaboration in decision-making processes.

Non-Markov Decision Processes and Reinforcement Learning
We present non-Markov decision processes, where rewards and dynamics can depend on the history of events. This is contrast with Markov Decision Processes, where the dependency is limited to the last state and action.

OzoBot
Ozobot is redefining the role of robotics in education with our award-winning coding robots and STEAM-based learning solutions.

SAFEXPLAIN Introduction to Trustworthy AI for Safety-Critical Systems
This introductory video provides an overview of the steps taken by the SAFEXPLAIN project to ensure that the AI-based solutions used in safety-critical systems are Trustworthy, explainable and comply with the safety guidelines of diverse industria

ALIGNER Fundamental Rights Impact Assessment
Artificial intelligence can enhance law enforcement agencies’ capabilities to prevent, investigate, detect and prosecute crimes, as well as to predict and anticipate them.