提出分层自适应共识网络，解决多智能体系统通信瓶颈与扩展性难题。
Hierarchical Adaptive Consensus Network: A Dynamic Framework for Scalable Consensus in Collaborative Multi-Agent AI Systems
- 分三层动态调整共识策略，依据任务特征与智能体表现灵活决策。
- 通信复杂度降至O(n)，实验显示共识阶段通信开销减少99.9%。
- 适合大规模协作场景，尤其适用于高动态、复杂任务的多智能体系统。
协同多智能体系统（MAS）中的共识策略面临适应性差、可扩展性弱和收敛不确定性等挑战。现有方法如结构化流程、辩论模型和迭代投票常导致通信瓶颈、严苛的决策机制和响应延迟。本文提出三层架构——分层自适应共识网络（HACN），根据任务特征和智能体性能指标动态生成共识策略。第一层收集多个局部智能体集群的置信度投票结果；第二层通过跨集群部分知识共享和动态超时机制实现集群间通信；第三层利用全局编排框架与可调决策规则进行全系统协调与最终仲裁。所提模型通信复杂度为$igO(n)$，优于现有全连接MAS的$igO(n^2)$。模拟实验表明，共识收敛期间通信开销降低99.9%。该方法通过层级升格与动态适应，确保了多种复杂任务下的共识收敛。
原文摘要 · Abstract (English)
The consensus strategies used in collaborative multi-agent systems (MAS) face notable challenges related to adaptability, scalability, and convergence certainties. These approaches, including structured workflows, debate models, and iterative voting, often lead to communication bottlenecks, stringent decision-making processes, and delayed responses in solving complex and evolving tasks. This article introduces a three-tier architecture, the Hierarchical Adaptive Consensus Network (\hacn), which suggests various consensus policies based on task characterization and agent performance metrics. The first layer collects the confidence-based voting outcomes of several local agent clusters. In contrast, the second level facilitates inter-cluster communication through cross-clustered partial knowledge sharing and dynamic timeouts. The third layer provides system-wide coordination and final arbitration by employing a global orchestration framework with adaptable decision rules. The proposed model achieves $\bigO(n)$ communication complexity, as opposed to the $\bigO(n^2)$ complexity of the existing fully connected MAS. Experiments performed in a simulated environment yielded a 99.9\% reduction in communication overhead during consensus convergence. Furthermore, the proposed approach ensures consensus convergence through hierarchical escalation and dynamic adaptation for a wide variety of complicated tasks.
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