智能路由算法动态优化多智能体系统路径选择
Adaptive routing protocols for determining optimal paths in AI multi-agent systems: a priority- and learning-enhanced approach
- 结合优先级与强化学习，动态调整路由权重
- 关键任务延迟降低，整体资源利用率提升
- 适合高负载、高实时性要求的AI系统
随着分布式人工智能和多智能体架构日益复杂，自适应、上下文感知的路由机制变得至关重要。本文提出一种面向AI多智能体网络的增强型自适应路由算法，融合基于优先级的成本函数与动态学习机制。在扩展的Dijkstra框架基础上，引入任务复杂度、用户请求优先级、代理能力、带宽、延迟、负载、模型复杂度和可靠性等多维参数。通过强化学习（RL）动态调节权重，持续优化路由策略以适应网络性能变化。此外，启发式过滤与分层路由结构提升了可扩展性与响应速度。该方法实现上下文敏感、负载感知、优先级导向的路由决策，不仅降低关键任务延迟，还优化整体资源利用，显著增强多智能体系统的鲁棒性、灵活性与效率。
原文摘要 · Abstract (English)
As distributed artificial intelligence (AI) and multi-agent architectures grow increasingly complex, the need for adaptive, context-aware routing becomes paramount. This paper introduces an enhanced, adaptive routing algorithm tailored for AI multi-agent networks, integrating priority-based cost functions and dynamic learning mechanisms. Building on an extended Dijkstra-based framework, we incorporate multi-faceted parameters such as task complexity, user request priority, agent capabilities, bandwidth, latency, load, model sophistication, and reliability. We further propose dynamically adaptive weighting factors, tuned via reinforcement learning (RL), to continuously evolve routing policies based on observed network performance. Additionally, heuristic filtering and hierarchical routing structures improve scalability and responsiveness. Our approach yields context-sensitive, load-aware, and priority-focused routing decisions that not only reduce latency for critical tasks but also optimize overall resource utilization, ultimately enhancing the robustness, flexibility, and efficiency of multi-agent systems.
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