How can I optimize multi-agent paths dynamically?
Adaptive routing protocols for determining optimal paths in AI multi-agent systems: a priority- and learning-enhanced approach
March 12, 2025
https://arxiv.org/pdf/2503.07686This paper proposes an adaptive routing algorithm (APBDA) for multi-agent AI systems, improving upon Dijkstra's algorithm by considering factors like task complexity, agent capabilities, and network conditions. It uses reinforcement learning to dynamically adjust route prioritization based on real-time performance feedback.
For LLM-based multi-agent systems, APBDA offers intelligent routing that considers LLM model sophistication and load, enabling efficient task distribution and resource management in complex, dynamic environments. It also improves scalability through heuristic filtering and hierarchical routing, crucial for managing large numbers of agents interacting with LLMs.