Project description
AI-based decision-making for critical systems operations
Artificial intelligence (AI) can be a powerful tool in the management of critical systems that have traditionally been under human control. The EU-funded AI4REALNET project will develop methods to prioritise trustworthiness in AI-assisted human control, incorporating augmented cognition, hybrid human-AI co-learning, and autonomous AI, all while maintaining a focus on the resilience, safety, and security of critical infrastructures. The project will also expedite the development and validation of new AI algorithms by the consortium and the broader AI community. It will do this by leveraging open-source AI-friendly digital environments capable of simulating realistic scenarios involving the operation of physical systems and human decision-making. Finally, AI4REALNET will contribute to addressing the critical aspects of decarbonisation, digitalisation, and resilience.
Objective
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.
Fields of science
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- engineering and technologycivil engineeringtransportation engineering
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectrical engineeringpower engineering
- social sciencessocial geographytransporttransport planningair traffic management
- natural sciencescomputer and information sciencesartificial intelligenceheuristic programming
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Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
4200 465 Porto
Portugal