AI for REAL-world NETwork operation (AI4REALNET)
Complementing and enhancing human capabilities with artificial intelligence with regard to the control of critical systems (electricity, railroad and air traffic management).
AI4REALNET designs AI-based solutions addressing critical systems (electricity, railway, and air traffic management) traditionally operated by humans, and where AI systems complement and augment human abilities.
1) to develop the next generation of decision-making methods powered by AI, 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.
2) to boost the development and validation of novel AI algorithms through existing open-source digital environments capable of emulating realistic scenarios of physical systems operation and human decision-making.
a) human-in-the-loop decision making for co-learning between AI and humans.
b) autonomous AI systems relying on human supervision, embedded with human domain knowledge and safety rules.
Team FHNW | Prof. Dr. Toni Wäfler (Lead), MSc Samira Hamouche, BSc Andrina Eisenegger, MSc Cyrill Ziegler |
Funding | AI4REALNET has received funding from the Swiss State Secretariat for Education, Research and lnnovation (SERI) and from European Union’s Horizon Europe Research and Innovation programme under the Grant Agreement No 101119527. |
Collaboration | DB Netz AG, EnliteAI GmbH, Fraunhofer-Gesellschaft EV, INESC TEC, linköpings universitet, NAV Portugal - Navegação Aérea, Politecnico di Milano, Réseau de Transport d’Electricité (RTE), Schweizerische Bundesbahnen SBB, TU Delft, Tennet TSO BV, Universität Kassel, Universiteit van Amsterdam, ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
Duration | October 2023 – March 2027 |
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