Descripción del proyecto
Toma de decisiones basada en IA para el funcionamiento de sistemas críticos
La inteligencia artificial (IA) puede ser una poderosa herramienta en la gestión de sistemas críticos que tradicionalmente han estado bajo control humano. El equipo del proyecto AI4REALNET, financiado con fondos europeos, desarrollará métodos para dar prioridad a la fiabilidad en el control humano asistido por IA, incorporando la cognición aumentada, el aprendizaje conjunto híbrido humano-IA y la IA autónoma, todo ello sin perder de vista la resiliencia, la seguridad y la protección de las infraestructuras críticas. En el proyecto también se acelerará el desarrollo y la validación de nuevos algoritmos de IA por parte del consorcio y de la comunidad de IA en general. Para ello, se utilizarán entornos digitales de código abierto compatibles con la IA y capaces de simular situaciones realistas en las que intervienen el funcionamiento de sistemas físicos y la toma de decisiones humanas. Por último, el equipo de AI4REALNET contribuirá a abordar los aspectos críticos de la descarbonización, la digitalización y la resiliencia.
Objetivo
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.
Ámbito científico
- 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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Régimen de financiación
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinador
4200 465 Porto
Portugal