Toward a more transparent and explainable conflict resolution algorithm for air traffic controllers Article Swipe
Christophe Hurter
,
Augustin Degas
,
Arnaud Guibert
,
Nicolas Durand
,
Mir Riyanul Islam
,
Shaibal Barua
,
Hamidur Rahman
,
Daniele Ruscio
,
Mobyen Uddin Ahmed
,
Shahina Begum
,
Stefano Bonelli
,
Giulia Cartocci
,
Gianluca Di Flumeri
,
Gianluca Borghini
,
Fabio Babiloni
,
Pietro Aricò
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.7148652
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.7148652
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. 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 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 for air traffic controllers. In this paper, we report our research directions and our findings to support better decision making with AI algorithm with extended transparency.
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- Type
- paratext
- Language
- en
- Landing Page
- https://doi.org/10.5281/zenodo.7148652
- OA Status
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4302018346
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4302018346Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.7148652Digital Object Identifier
- Title
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Toward a more transparent and explainable conflict resolution algorithm for air traffic controllersWork title
- Type
-
paratextOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-31Full publication date if available
- Authors
-
Christophe Hurter, Augustin Degas, Arnaud Guibert, Nicolas Durand, Mir Riyanul Islam, Shaibal Barua, Hamidur Rahman, Daniele Ruscio, Mobyen Uddin Ahmed, Shahina Begum, Stefano Bonelli, Giulia Cartocci, Gianluca Di Flumeri, Gianluca Borghini, Fabio Babiloni, Pietro AricòList of authors in order
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https://doi.org/10.5281/zenodo.7148652Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.7148652Direct OA link when available
- Concepts
-
Computer science, Conflict resolution, Air traffic control, Resolution (logic), Algorithm, Artificial intelligence, Engineering, Aerospace engineering, Political science, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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