Abstract
(Englisch)
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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. It has two main strategic goals: 1) to develop the next
generation of decision-making methods powered by supervised and reinforcement learning, which aim at trustworthiness
in AI-assisted human control with augmented cognition, hybrid human-AI co-learning and autonomous AI, with
the resilience, safety, and security of critical infrastructures as core requirements, and 2) to boost the development
and validation of novel AI algorithms, by the consortium and AI community, through existing open-source digital
environments capable of emulating realistic scenarios of physical systems operation and human decision-making.
The core elements are: a) 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; b) human-in-the-loop
decision making for co-learning between AI and humans, considering integration of model uncertainty, human cognitive
load and trust; c) autonomous AI systems relying on human supervision, embedded with human domain knowledge and
safety rules.
The AI4REALNET framework will be validated in 6 uses cases driven by industry requirements, across 3 network
infrastructures with common properties. The use cases are focused on critical challenges and tasks of network operators,
considering strategic long-term goals, such as decarbonisation, digitalisation, and resilience to disturbances, and are
formulated in a unified sequential decision problem where many AI and non-AI algorithms can be applied and
benchmarked.
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