Collaborative Feature Analysis of UAV Swarm Task Based on Causal Emergence Theory

Aiming at the problem that the cooperative behavior of UAV swarms in dynamic environments is hard to quantitatively evaluate, this paper proposes a quantitative method based on the causal emergence theory. Firstly, a microscopic model involving individual dynamics is constructed, and the mapping from microscopic states to macroscopic features is achieved by designing micro-macro indicators. On this basis, the effective information at both micro and macro levels is defined and calculated, and the occurrence of emergent behavior in the swarm is determined by causal gain. Finally, the causal emergence index is correlated with task success rate to verify its effectiveness as a new dimension for evaluating collaboration quality. Meanwhile, the influence of different task ratios on emergence values is explored. The research results show that the proposed method can effectively quantify the impact of emergent behavior on swarm tasks, providing theoretical support and technical references for the collaborative optimization of UAV swarm tasks.

Keywords: UAV swarm, Causal emergence, Coarse-graining, Task collaboration, Feature analysis, Effective information.