Learning air traffic controller strategies with demonstration-based and physiological feedback
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 learni...
Saved in:
Main Authors: | Pham, Duc-Thinh, Tran, Ngoc Phu, Goh, Sim Kuan, Ma, Chunyao, Alam, Sameer, Duong, Vu |
---|---|
Other Authors: | School of Mechanical and Aerospace Engineering |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/145645 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
A machine learning approach for conflict resolution in dense traffic scenarios with uncertainties
by: Pham, Duc-Thinh, et al.
Published: (2021) -
Reinforcement learning for two-aircraft conflict resolution in the presence of uncertainty
by: Pham, Duc-Thinh, et al.
Published: (2020) -
An intelligent interactive conflict solver incorporating air traffic controllers' preferences using reinforcement learning
by: Tran, Ngoc Phu, et al.
Published: (2020) -
An interactive conflict solver for learning air traffic conflict resolutions
by: Tran, Ngoc Phu, et al.
Published: (2020) -
On robustness paradox in air traffic networks
by: Cai, Qing, et al.
Published: (2021)