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Transparent Artificial Intelligence and Automation to Air Traffic Management Systems: Conflict Detection and Resolution

Authors:

Christophe Hurter , Augustin Degas , Mir Riyanul Islam, Shaibal Barua, Hamidur Rahman, Minesh Poudel , Daniele Ruscio , Mobyen Uddin Ahmed, Shahina Begum, Md Aquif Rahman, Stefano Bonelli , Giulia Cartocci , Gianluca Di Flumeri , Gianluca Borghini , Pietro Aricò , Fabio Babiloni

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Conference/Workshop Paper

Venue:

International Conference on Cognitive Aircraft Systems ICCAS

Abstract

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. In order to address this challenge related to transparency of the automated system in the ATM domain, we investigated AI methods in predicting air transportation traffic conflict based on the domain of Explainable Artificial Intelligence (XAI).

Bibtex

@inproceedings{Hurter6554,
author = {Christophe Hurter and Augustin Degas and Mir Riyanul Islam and Shaibal Barua and Hamidur Rahman and Minesh Poudel and Daniele Ruscio and Mobyen Uddin Ahmed and Shahina Begum and Md Aquif Rahman and Stefano Bonelli and Giulia Cartocci and Gianluca Di Flumeri and Gianluca Borghini and Pietro Aric{\`o} and Fabio Babiloni},
title = {Transparent Artificial Intelligence and Automation to Air Traffic Management Systems: Conflict Detection and Resolution},
month = {August},
year = {2022},
booktitle = {International Conference on Cognitive Aircraft Systems ICCAS},
url = {http://www.es.mdu.se/publications/6554-}
}