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ARTIMATION

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TRANSPARENT ARTIFICIAL INTELLIGENCE AND AUTOMATION TO AIR TRAFFIC MANAGEMENT SYS...

TRANSPARENT ARTIFICIAL INTELLIGENCE AND AUTOMATION TO AIR TRAFFIC MANAGEMENT SYSTEMS Recently, Artificial intelligence (AI) algorithms have shown increasable interest in various application domains including in Air Transportation Management (ATM). Different AI in particular Machine Learning (ML) algorithms are use... see more

31/12/2022

MDU

999K€

Project Budget: 999K€

Project leader

MALARDALENS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

PARTICIPATION DEPralty Sin fecha límite de participación.

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Financing granted El organismo H2020 notifico la concesión del proyecto The day 2022-12-31

SESAR-ER4-01-2019: Digitalisation and Automation princ... Specific Challenge:Increasing the Automation in ATM is considered as a key to significantly improve...

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characteristics of the participant

Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Private Information

No hay información privada compartida para este proyecto. Habla con el coordinador.

4 Participantes

MDU

350.00K€ | Leader

ENAC

245.00K€ | participant

UNIROMA1

192.50K€ | participant

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Project duration: 26 months Date Start: 2020-10-19
End date: 2022-12-31

Project leader

MALARDALENS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

Project Budget 999K€

participation deadline Sin fecha límite de participación.

Project description Recently, Artificial intelligence (AI) algorithms have shown increasable interest in various application domains including in Air Transportation Management (ATM). Different AI in particular Machine Learning (ML) algorithms are used to provide decision support in autonomous decision-making tasks in the ATM domain e.g. predicting air transportation traffic and optimizing traffic flows. However, most of the time these automated systems are not accepted or trusted by the intended users as the decisions provided by AI are often opaque, non-intuitive and not understandable by human operators. Safety is the major pillar to air traffic management, and no black box process can be inserted in a decision-making process when human life is involved. In order to address this challenge related to transparency of the automated system in the ATM domain, ARTIMATION focuses on investigating AI methods in predicting air transportation traffic and optimizing traffic flows based on the domain of Explainable Artificial Intelligence (XAI). Here, AI models’ explainability in terms of understanding a decision i.e., post hoc interpretability and understanding how the model works i.e., transparency can be provided in the air traffic management. In predicting air transportation traffic and optimizing traffic flows systems, ARTIMATION will provide a proof-of-concept of transparent AI models that includes visualization, explanation, generalization with adaptability over time to ensure safe and reliable decision support.

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