CBR and Focus Learning based Joint Fire Strike Plan Generation Method
Authors: 
Xin Jin, Xinnian Wang, Fei Cai, Yupu GuoAuthors Info & Claims
Pages 210 - 217
Abstract
It is a common military activity to carry out joint fire strike against sea/air-based targets with high threat/value but strong defense ability. The especially high time sensitivity requires immediate actions, leaving little time for planning, greatly challenging the commanders’ experience and ability of to work under pressure. This will be changed by AI technologies. A CBR and focus learning based joint fire strike plan generation method is proposed. In peacetime, the scenarios and planning products that the operational staff study and drill are accumulated. The system will automatically recommend reference cases suitable for the current situation according to the new task and battlefield situation, and incrementally learn the concerns of the proficient staff on reference case selection facing tasks. The method has been verified feasible and effective through experiments, which can generate joint strike plans in seconds, and significantly reduce the error rate of the novice staffs, with certain reference value to the command information system development.
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Index Terms
- CBR and Focus Learning based Joint Fire Strike Plan Generation Method
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Published In
August 2022
241 pages
ISBN:9781450397315
DOI:10.1145/3561613
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 November 2022
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ICCCV 2022
ICCCV 2022: 2022 The 5th International Conference on Control and Computer Vision
August 19 - 21, 2022
Xiamen, China
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