An intelligent interactive conflict solver incorporating air traffic controllers' preferences using reinforcement learning
The increasing demand in air transportation is pushing the current air traffic management (ATM) system to its limits in the airspace capacity and workload of air traffic controllers (ATCOs). ATCOs are in an urgent need of assistant tools to aid them in dealing with increased traffic. To address this...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/144398 |
| _version_ | 1811695122001166336 |
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| author | Tran, Ngoc Phu Pham, Duc-Thinh Goh, Sim Kuan Alam, Sameer Duong, Vu |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Tran, Ngoc Phu Pham, Duc-Thinh Goh, Sim Kuan Alam, Sameer Duong, Vu |
| author_sort | Tran, Ngoc Phu |
| collection | NTU |
| description | The increasing demand in air transportation is pushing the current air traffic management (ATM) system to its limits in the airspace capacity and workload of air traffic controllers (ATCOs). ATCOs are in an urgent need of assistant tools to aid them in dealing with increased traffic. To address this issue, the application of artificial intelligence (AI) in supporting ATCOs is a promising approach. In this work, we build an AI system as a digital assistant to support ATCOs in resolving potential conflicts. Our system consists of two core components: an intelligent interactive conflict solver (iCS) to acquire ATCOs' preferences, and an AI agent based on reinforcement learning to suggest conflict resolutions capturing those preferences. We observe that providing the AI agent with the human resolutions, which are acquired and characterized by our intelligent interactive conflicts solver, not only improves the agent's performance but also gives it the capability to suggest more human-like resolutions, which could help increase the ATCOs' acceptance rate of the agent's suggested resolutions. Our system could be further developed as personalized digital assistants of ACTOs to maintain their workloads manageable when they have to deal with sectors with increased traffic. |
| first_indexed | 2024-10-01T07:18:26Z |
| format | Conference Paper |
| id | ntu-10356/144398 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:18:26Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1443982023-03-04T17:07:57Z An intelligent interactive conflict solver incorporating air traffic controllers' preferences using reinforcement learning Tran, Ngoc Phu Pham, Duc-Thinh Goh, Sim Kuan Alam, Sameer Duong, Vu School of Mechanical and Aerospace Engineering 2019 Integrated Communications, Navigation and Surveillance Conference (ICNS) Air Traffic Management Research Institute Engineering::Aeronautical engineering Air Traffic Management Conflict Resolution The increasing demand in air transportation is pushing the current air traffic management (ATM) system to its limits in the airspace capacity and workload of air traffic controllers (ATCOs). ATCOs are in an urgent need of assistant tools to aid them in dealing with increased traffic. To address this issue, the application of artificial intelligence (AI) in supporting ATCOs is a promising approach. In this work, we build an AI system as a digital assistant to support ATCOs in resolving potential conflicts. Our system consists of two core components: an intelligent interactive conflict solver (iCS) to acquire ATCOs' preferences, and an AI agent based on reinforcement learning to suggest conflict resolutions capturing those preferences. We observe that providing the AI agent with the human resolutions, which are acquired and characterized by our intelligent interactive conflicts solver, not only improves the agent's performance but also gives it the capability to suggest more human-like resolutions, which could help increase the ATCOs' acceptance rate of the agent's suggested resolutions. Our system could be further developed as personalized digital assistants of ACTOs to maintain their workloads manageable when they have to deal with sectors with increased traffic. Civil Aviation Authority of Singapore (CAAS) Accepted version This research / project* is supported by the Civil Aviation Authority of Singapore and Nanyang Technological University, Singapore under their collaboration in the Air Traffic Management Research Institute. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Civil Aviation Authority of Singapore. 2020-11-03T05:34:08Z 2020-11-03T05:34:08Z 2019 Conference Paper Tran, N. P., Pham, D.-T., Goh, S. K., Alam, S., & Duong, V. (2020). An intelligent interactive conflict solver incorporating air traffic controllers' preferences using reinforcement learning. Proceedings of the 2019 Integrated Communications, Navigation and Surveillance Conference (ICNS). doi:10.1109/ICNSURV.2019.8735168 https://hdl.handle.net/10356/144398 10.1109/ICNSURV.2019.8735168 en © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work is available at: https://doi.org/10.1109/ICNSURV.2019.8735168 application/pdf |
| spellingShingle | Engineering::Aeronautical engineering Air Traffic Management Conflict Resolution Tran, Ngoc Phu Pham, Duc-Thinh Goh, Sim Kuan Alam, Sameer Duong, Vu An intelligent interactive conflict solver incorporating air traffic controllers' preferences using reinforcement learning |
| title | An intelligent interactive conflict solver incorporating air traffic controllers' preferences using reinforcement learning |
| title_full | An intelligent interactive conflict solver incorporating air traffic controllers' preferences using reinforcement learning |
| title_fullStr | An intelligent interactive conflict solver incorporating air traffic controllers' preferences using reinforcement learning |
| title_full_unstemmed | An intelligent interactive conflict solver incorporating air traffic controllers' preferences using reinforcement learning |
| title_short | An intelligent interactive conflict solver incorporating air traffic controllers' preferences using reinforcement learning |
| title_sort | intelligent interactive conflict solver incorporating air traffic controllers preferences using reinforcement learning |
| topic | Engineering::Aeronautical engineering Air Traffic Management Conflict Resolution |
| url | https://hdl.handle.net/10356/144398 |
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