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- W3035993313 abstract "The increasing in traffic demand has strained air traffic control system and controllers which lead to the need of novel and efficient conflict detection and resolution advisory. In the scope of this thesis, we concentrate on studying challenges in conflict detection and resolution by using machine learning approaches. We have attempted to learn and predict controller behaviors from data using Random Forest. We also propose a novel approach for probabilistic conflict detection by using Heteroscedastic Gaussian Process as predictive models and Bayesian Optimization for probabilistic conflict detection algorithm. Finally, we propose an artificial intelligent agent that is capable of resolving conflicts, in the presence of traffic and uncertainty. The conflict resolution task is formulated as a decision-making problem in large and complex action space, which is applicable for employing reinforcement learning algorithm. Our work includes the development of a learning environment, scenario state representation, reward function, and learning algorithm. Machine learning methods have showed their advantages and potential in conflict detection and resolution related challenges. However, more studies would be conducted to improve their performances such as airspace network representation, multi-agent reinforcement learning or controller's strategy reconstruction from data." @default.
- W3035993313 created "2020-06-25" @default.
- W3035993313 creator A5091693318 @default.
- W3035993313 date "2019-06-21" @default.
- W3035993313 modified "2023-09-26" @default.
- W3035993313 title "Machine learning-based flight trajectories prediction and air traffic conflict resolution advisory" @default.
- W3035993313 hasPublicationYear "2019" @default.
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