🤖 AI Summary
To address state oscillation and deadlock issues inherent in rule-based decision-making (e.g., finite state machines) for dynamic swarm adversarial scenarios, this paper proposes an interpretable, stable, and adaptive collective intelligence decision framework. Methodologically, it innovatively integrates probabilistic finite state machines (PFSMs), deep convolutional neural networks (CNNs), and multi-agent deep reinforcement learning (MARL): PFSMs ensure decision interpretability and state consistency; CNNs extract high-dimensional environmental features; and MARL enables cooperative policy optimization. Mechanistically, the framework eliminates the root causes of conflicts arising from deterministic state transitions. Experimental results in realistic adversarial settings demonstrate significant improvements in decision stability and task success rate. Specifically, the proposed framework achieves higher human-likeness in collaborative and competitive swarm strategies, superior task completion rates, and enhanced robustness compared to state-of-the-art approaches.
📝 Abstract
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.