解决群体对抗中的决策冲突，实现稳定可靠的智能决策。
Rule-Based Conflict-Free Decision Framework in Swarm Confrontation
- 融合概率有限状态机、深度卷积网络与强化学习的新型决策框架
- 实验证明其在群体对抗中表现更优，有效避免抖动与死锁
- 适合需要可解释性智能的复杂动态场景应用
传统基于规则的决策方法（如有限状态机）在高度动态场景中易出现抖动或死锁（JoD）问题。为实现智能体群体对抗，需解决导致大量JoD问题的决策冲突。本文提出一种新决策框架，结合概率有限状态机、深度卷积网络与强化学习，将可解释智能引入智能体。该框架克服了状态机不稳定性与JoD问题，在真实实验的严格评估中表现出色，展现出类人协作与竞争策略优势，优于现有方法。
原文摘要 · Abstract (English)
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.
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