Traditional rule--based decision--making methods with interpretable advantage, such as finite state machine, suffer from the jitter or deadlock(JoD) problems in extremely dynamic scenarios. To realize agent swarm confrontation, decision conflicts causing many JoD problems are a key issue to be solved. Here, we propose a novel decision--making framework that integrates probabilistic finite state machine, deep convolutional networks, and reinforcement learning to implement interpretable intelligence into agents. Our framework overcomes state machine instability and JoD problems, ensuring reliable and adaptable decisions in swarm confrontation. The proposed approach demonstrates effective performance via enhanced human--like cooperation and competitive strategies in the rigorous evaluation of real experiments, outperforming other methods.

本研究解决了传统基于规则的决策方法在动态场景中遇到的抖动或死锁问题，特别是在代理群体对抗中的决策冲突。提出的框架结合了概率有限状态机、深度卷积网络和强化学习，实现了代理的可解释智能，显著提高了人类般的合作与竞争策略，实验结果显示其性能优于其他方法。

基于规则的无冲突决策框架在群体对抗中的应用