Deep AI military staff: cooperative battlefield situation awareness for commander’s decision making

There are many studies adopting artificial intelligence (AI) to develop core technologies for the future army but they are still at the level of basic research. It is expected that military power will be negatively affected by aging and declining population. In addition, as more than 500,000 agents...

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Bibliographic Details
Published in:The Journal of supercomputing 2023-04, Vol.79 (6), p.6040-6069
Main Authors: Lee, Chang-Eun, Baek, Jaeuk, Son, Jeany, Ha, Young-Guk
Format: Article
Language:English
Subjects:
Advances in Big Data and Deep Learning
Agents (artificial intelligence)
Artificial intelligence
Battlefields
Compilers
Computer Science
Decision making
Interpreters
Multiagent systems
Processor Architectures
Programming Languages
Reagents
Situational awareness
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Summary:There are many studies adopting artificial intelligence (AI) to develop core technologies for the future army but they are still at the level of basic research. It is expected that military power will be negatively affected by aging and declining population. In addition, as more than 500,000 agents will be dispatched to monitor combat scenes, the data sensed by each agent should be managed simultaneously recognize and evaluate the situation on the battlefield in real time. Despite increased complexity in the battlefield, current command system entirely rely on the experience and expertise of individual commanders, which severely restricts defense capabilities. Therefore, AI based military staff needs to be developed to identify potential threats that commanders are likely to miss, to develop smart command systems, and to provide data-driven rationale for commander’s decisions. In this paper, we propose a deep AI military staff to support commander decision-making. Our proposed model is composed of four key parts: multi-agent based manned-unmanned collaboration architecture (MACA), robust tactical map fusion technology in poor environments (RTMF), hypergraph based representation learning (HRL) and space-time multi layer model for battlefields recognition (STBR). We design an architecture and generate dataset for training the core network. Simulation results are provided to demonstrate the performance of Deep AI military staff.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-022-04882-w