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Zhaoqi Dong

Publications and source records attributed to Zhaoqi Dong.

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Various Vicsek Models with Underlying Network Characteristics

Collective motion is a fundamental phenomenon in biological swarms. As a framework for studying synchronization in motions, the Vicsek model is simple and efficient, assuming isotropic interactions with a complete field of view. Drawing inspiration from natural swarms, we incorporate realistic constraints into the model. By analysing the interaction structures from the complex network perspective, we demonstrate that models with the homogeneous interaction rules naturally form Erdos-Renyi networks, whereas the introduction of heterogeneity leads to Barabasi-Albert networks. Furthermore, we discover that the model's synchronization is fundamentally governed by the average degree of the interaction network. Through a comparative analysis across these topologies, we identify a stretched-exponential relationship between the average degree and the synchronization metrics.

physics.soc-ph

Rule-Based Conflict-Free Decision Framework in Swarm Confrontation

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.

cs.AI