战斗决策中的人工智能:通过强化学习和图形神经网络来分配武器目标
IEEE transactions on cybernetics
|October 20, 2025
概括
这项研究引入了一种新的方法,使用深度增强学习和图形神经网络来优化战场上的动态武器-目标分配 (DWTA) 决策,提高可扩展性和相关性.
科学领域:
- 人工智能的人工智能
- 运营研究 运营研究
- 军事科学 军事科学
背景情况:
- 动态武器目标分配 (DWTA) 对于战场上的成功至关重要.
- 目前用于DWTA的深度增强学习 (DRL) 方法在表示战场拓,可扩展性和性能指标相关性方面面临挑战.
研究的目的:
- 为了解决现有的DWTA方法的局限性.
- 使用DRL和图形神经网络 (GNN) 开发一个改进的DWTA解决方案.
主要方法:
- 设计了一个全新的部分可观测的马尔科夫决策过程 (POMDP) 框架.
- 集成基于图形的动作表示,观察特征和奖励设计.
- 借助DRL和GNN进行先进的决策.
主要成果:
- 拟议的DRL和GNN方法在海上和地面战斗领域都表现出了有效性.
- 在DWTA中表现优于现有的启发式和元启发式方法.
- 综合验证证实了GNN和决策模式的有效性.
结论:
- 集成的DRL和GNN框架在解决DWTA问题方面取得了重大进展.
- 新的POMDP设计增强了复杂军事行动的可扩展性和相关性.
- 这种方法为战略战场决策提供了更强大,更有效的解决方案.
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