The scope of AI4REALNET covers the perspective of AI-based solutions addressing critical systems (electricity, railway, and air traffic management) modelled by networks that can be simulated, and are traditionally operated by humans, and where AI systems complement and augment human abilities.
Our core elements are:
AI algorithms mainly composed by supervised and reinforcement learning.
Unifying the benefits of existing heuristics, physical modelling of these complex systems and learning methods, as well as a set of complementary techniques to enhance transparency, safety, explainability and human acceptance.
Human-in-the-loop decision making for co-learning between AI and humans.
Considering integration of model uncertainty, human cognitive load and trust.
Autonomous AI systems relying on human supervision.
Embedded with human domain knowledge and Safety rules.