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Toward a more transparent and explainable conflict resolution algorithm for air traffic controllers
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 thedoi:10.5281/zenodo.7148652 fatcat:opvrkzn6wvenni6gzptvatj44y