Featured outcome

In Air Transportation Management domain, AI and ML algorithms have shown increasable interest. Different AI and ML algorithms are used to provide decision support in autonomous Decision Making tasks, e.g. optimizing traffic flows. The most problematic consequence of supporting ATCOs’ Decision Making with AI/ML algorithms is that most of the time these automated systems are not trusted by the users. The outcome of AI Decision Making is in fact currently a “black box”. ARTIMATION project focuses then on the domain of Explainable Artificial Intelligence (XAI) to ensure safe and reliable decision support. ARTIMATION’s goal is providing a transparent and explainable AI model through visualization, data driven storytelling and immersive analytics.

During the first six months, the Consortium successfully completed the first phase of the project producing the first deliverables (see First Half-Year Consortium Meeting and the Deliverable “D1.1 – Project Management Plan”).

As for M7, the Consortium is also ready to publish the D3.1: State of Art – AI support in ATM. In this document the Consortium has identified the transparency in AI algorithm based on a systematic literature review of the most relevant topics on AI explainability in ATM domain.

Also the Communication, Dissemination and exploitation plan (D8.1) is about to being submitted.

These firsts six months have ended with the T3.2 Workshop: Tasks to be supported by AI algorithms. The main goal of the workshop was generating a prioritized list of ATCOs tasks to be helped by AI algorithms. The Consortium during the workshop has also collected some use stories and scenarios that will be helpful for the validation activities of ARTIMATION algorithms.


Featured deliverable: The project deliverable containing the Project Management Plan is available here.