Rule-Based Conflict-Free Decision Framework in Swarm Confrontation
This addresses decision-making issues in swarm confrontation for robotics or multi-agent systems, but appears incremental as it combines existing methods.
The paper tackled the problem of decision conflicts causing jitter or deadlock in rule-based swarm confrontation by proposing a framework integrating probabilistic finite state machines, deep convolutional networks, and reinforcement learning, resulting in effective performance with enhanced cooperation and strategies in real experiments.
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.