Hierarchical CBR for multiple aircraft conflict resolution in air traffic control

被引:0
|
作者
Bonzano, A [1 ]
Cunningham, P [1 ]
机构
[1] Trinity Coll, Dept Comp Sci, Dublin, Ireland
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper we present a Case-Based Reasoning system that helps air traffic controllers to solve aircraft conflicts. In particular, we focus on the hierarchical aspect of the CBR system which is able to solve multiple aircraft conflicts, i.e. conflicts that involve three or more aircraft. It is not practical to build a case-base for the different multiple aircraft possibilities as has been done for two aircraft conflicts. Instead we explore the possibility of using case fragments from two aircraft conflicts in multiple aircraft situations. The hierarchical structure that we describe here makes this possible. This involves the use of some high-level analysis of the solutions coming from the case base because the solution to a multiple aircraft conflict is not necessarily one of the solutions of the component two aircraft conflicts. The hierarchical structure allows the use of the same case-base for both two aircraft conflicts and multiple aircraft conflicts with big savings in space and time.
引用
收藏
页码:58 / 62
页数:5
相关论文
共 50 条
  • [21] A framework for conflict resolution in air traffic management
    Resmerita, S
    Heymann, M
    Meyer, G
    42ND IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-6, PROCEEDINGS, 2003, : 2035 - 2040
  • [22] A model of air traffic controllers' conflict detection and conflict resolution
    Eyferth, K
    Niessen, C
    Spaeth, O
    AEROSPACE SCIENCE AND TECHNOLOGY, 2003, 7 (06) : 409 - 416
  • [23] Deep reinforcement learning based conflict detection and resolution in air traffic control
    Wang, Zhuang
    Li, Hui
    Wang, Junfeng
    Shen, Feng
    IET INTELLIGENT TRANSPORT SYSTEMS, 2019, 13 (06) : 1041 - 1047
  • [24] Benefits of Imperfect Conflict Resolution Advisory Aids for Future Air Traffic Control
    Trapsilawati, Fitri
    Wickens, Christopher D.
    Qu, Xingda
    Chen, Chun-Hsien
    HUMAN FACTORS, 2016, 58 (07) : 1007 - 1019
  • [25] Provably Safe Conflict Resolution With Bounded Turn Rate for Air Traffic Control
    Yoo, Jeff
    Devasia, Santosh
    IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2013, 21 (06) : 2280 - 2289
  • [26] DYNAMIC PLANNING AND TIME-CONFLICT RESOLUTION IN AIR-TRAFFIC-CONTROL
    VOLCKERS, U
    LECTURE NOTES IN CONTROL AND INFORMATION SCIENCES, 1986, 80 : 175 - 197
  • [27] Review of Deep Reinforcement Learning Approaches for Conflict Resolution in Air Traffic Control
    Wang, Zhuang
    Pan, Weijun
    Li, Hui
    Wang, Xuan
    Zuo, Qinghai
    AEROSPACE, 2022, 9 (06)
  • [28] An Investigation into Conflict Resolution and Trajectory Prediction Aids for Future Air Traffic Control
    Trapsilawati, Fitri
    Chen, Chun-Hsien
    Khoo, Li Pheng
    TRANSDISCIPLINARY ENGINEERING: CROSSING BOUNDARIES, 2016, 4 : 503 - 512
  • [29] Conflict monitoring in air traffic control
    Sheridan, TB
    Park, I
    Meyer, J
    Juergensohn, T
    Landry, SJ
    Yufik, YM
    ANALYSIS, DESIGN AND EVALUATION OF HUMAN-MACHINE SYSTEMS 2001, 2002, : 543 - 548
  • [30] ISAC: A CBR system for decision support in air traffic control
    Bonzano, A
    Cunningham, P
    Meckiff, C
    ADVANCES IN CASE-BASED REASONING, 1996, 1168 : 44 - 57