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Found 6 papers, 1 papers with code
Explainable Deep Reinforcement Learning: State of the Art and Challenges
In this article we aim to provide a review of state of the art methods for explainable deep reinforcement learning methods, taking also into account the needs of human operators - i. e., of those that take the actual and critical decisions in solving real-world problems.
Data-driven prediction of Air Traffic Controllers reactions to resolving conflicts
With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the Air Traffic Management domain, in this paper we propose deep learning techniques (DL) that can learn models of Air Traffic Controllers' (ATCO) reactions in resolving conflicts that can violate separation minimum constraints among aircraft trajectories: This implies learning when the ATCO will react towards resolving a conflict, and how he/she will react.
Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning
Towards this goal we present a comprehensive framework comprising the Generative Adversarial Imitation Learning state of the art method, in a pipeline with trajectory clustering and classification methods.
Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods
In this article, we report on the efficiency and effectiveness of multiagent reinforcement learning methods (MARL) for the computation of flight delays to resolve congestion problems in the Air Traffic Management (ATM) domain.
Combining Ontologies with Correspondences and Link Relations: The E-SHIQ Representation Framework
In this article, we moti- vate the need for a representation framework that allows peers to combine their knowledge in various ways, maintaining the subjectivity of their own knowledge and beliefs, and that reason collaboratively, constructing a tableau that is distributed among them, jointly.
Probabilistic Event Calculus for Event Recognition
In a typical event recognition application, however, these systems often have to deal with a significant amount of uncertainty.
