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.

• Name: INESC TEC - Institute for Systems and Computer Engineering, Technology and Science
• Country: Portugal

https://cordis.europa.eu/project/id/101119527

The research leading to this work is being carried out as a part of the AI4REALNET (AI for REAL-world NETwork operation) project, European Union’s Horizon Research and Innovation Programme, Grant Agreement No. 101119527. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

We are dedicated to creating a community where everyone feels valued and included. To achieve this goal, we have developed a set of open-source guidelines that promote good practices in the key aspects of an open-source organization, from Code Development to Quality Control, Risk Identification, and Mitigation.

We strongly recommend that you start by reading our guidelines and principles on How To Contribute to Open-Source.

Please note that compliance with this organization's Code of Conduct is required for anyone participating in AI4REALNET's open-source initiatives.

If you discover any security problems or vulnerabilities in the source code of any repository belonging to this organization, please report them in accordance with the guidelines outlined in the Security document.

Please forward your questions to ai4realnet.oss@lists.inesctec.pt