Description du projet
Prise de décision basée sur l’IA pour les opérations de systèmes critiques
L’intelligence artificielle (IA) peut être un outil puissant dans le cadre de la gestion de systèmes critiques traditionnellement sous contrôle humain. Le projet AI4REALNET, financé par l’UE, élaborera des méthodes permettant de donner la priorité à la confiance dans le contrôle humain assisté par l’IA, en incorporant la cognition augmentée, le coapprentissage hybride humain-AI et l’IA autonome, tout en maintenant l’accent sur la résilience, la sûreté et la sécurité des infrastructures critiques. Le projet accélérera également le développement et la validation de nouveaux algorithmes par le consortium et la communauté de l’IA au sens large. Pour ce faire, il s’appuiera sur des environnements numériques à source ouverte adaptés à l’IA et capables de simuler des scénarios réalistes impliquant le fonctionnement de systèmes physiques et la prise de décisions par l’homme. Enfin, AI4REALNET contribuera à traiter les aspects critiques de la décarbonation, de la numérisation et de la résilience.
Objectif
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.
Champ scientifique
- 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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HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinateur
4200 465 Porto
Portugal