Abstract: 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.

Key words: situationassessment, human-machineconsensus, robustdecisionmaking, combatintentrecognition, human-machineintelligenceintegration