Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145645
Title: Learning air traffic controller strategies with demonstration-based and physiological feedback
Authors: Pham, Duc-Thinh
Tran, Ngoc Phu
Goh, Sim Kuan
Ma, Chunyao
Alam, Sameer
Duong, Vu
Keywords: Engineering::Aeronautical engineering
Issue Date: 2018
Source: Pham, D.-T., Tran, N. P., Goh, S. K., Ma, C., Alam, S., & Duong, V. (2018). Learning air traffic controller strategies with demonstration-based and physiological feedback. Proceedings of the SESAR Innovation Days 2018 (SIDS).
Abstract: In this research, we demonstrate an Artificial Intelligence framework that is able to learn conflict resolution strategies from human Air Traffic Controllers and then employ such knowledge in developing conflict resolution advisories. The proposed framework is designed to assist reinforcement learning, using conflict resolution actions and brain signals. By involving human-in-the-loop in the training, the Artificial Intelligence framework is expected to generate conflict resolution advisories with high acceptability. Our preliminary results have shown the ability of our framework in learning Air Traffic Controllers' strategy and providing human-like resolutions.
URI: https://hdl.handle.net/10356/145645
Rights: © 2018 Air Traffic Management Research Institute. All rights reserved. This paper was published in SESAR Innovation Days 2018 (SIDS) and is made available with permission of Air Traffic Management Research Institute.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers

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