面向鲁棒决策的战场态势评估人机共识形成方法

针对地面突击装备战术级、高动态、分布式、强实时作战过程中的战场态势高度复杂且信息不确定问题，提出一种面向鲁棒决策的战场态势评估人机共识形成方法，实现“人类智能”与“人工智能”在综合态势评估中的有机融合。首先，通过采集乘员（专家）态势评估行为实验样本，模拟乘员（专家）对战场态势信息的认知过程，构建战场态势评估两级智能代理模型；其次，提出一种对人机偏好不敏感的决策鲁棒指数，车辆乘员基于该指数在高强度对抗下快速判断形成人机共识，辅助指挥员进行鲁棒决策。在某作战仿真系统中进行了红蓝对抗的想定设计，并以敌方意图为态势评估对象进行了案例验证，验证了该方法的有效性。

A Human-Machine Consensus Formation Method for Robust Decision Making in Battlefield Situation Assessment

Ground assault equipment tactical-level, highly dynamic, distributed, and strong real-time operations have the characteristics of battlefield complexity and information uncertainty, to address these characteristics this paper proposes a human-machine consensus formation method for battlefield situation assessment robust decision making, which fuses “human intelligence” and “artificial intelligence” to achieve comprehensive situational assessment. First, by collecting experimental samples of crew members (‘experts’) situational assessment behaviors and simulating their cognitive process of battlefield situational information, a two-level intelligent agent model for battlefield situational assessment is constructed. Then, a decision robustness index that is insensitive to human-machine preferences is proposed, based on which vehicle crew members quickly judge and form human-machine consensus under high-intensity confrontation, and assist commanders in making robust decisions. We design a red-blue confrontation scenario in a battle simulation system, and use an enemy combat intent recognition example to test the utility of the proposed method.