How to optimize multi-agent data delivery with limited comms?
Multi-agent coordination for data gathering with periodic requests and deliveries
March 25, 2025
https://arxiv.org/pdf/2503.18546This paper explores coordinating multiple autonomous agents (robots/software agents) to collect data from dynamic locations and deliver it to a central hub (OC). Agents have limited communication range, requiring them to physically travel to the OC or intermediary "collector" agents. The research focuses on optimizing data refreshing time by balancing worker and collector agents and dynamically assigning work areas.
Key points for LLM-based multi-agent systems:
- Decentralized coordination: The algorithm can be executed by the central hub or distributed among the agents. This is relevant to LLM agents which could operate independently or with a central coordinator.
- Dynamic task allocation: The algorithm dynamically assigns tasks (data collection locations) and rendezvous points. This translates well to LLM agents which can handle dynamic instructions and adapt to changing environments.
- Optimization objective: The research focuses on optimizing a specific objective (data freshness). This is analogous to how LLM agents in a web application could be directed towards a particular goal, such as maximizing user engagement or streamlining content generation.
- Path planning & Resource allocation: The paper uses path planning algorithms to determine efficient routes and allocate resources (agents). This aligns with potential uses of LLMs to plan and coordinate actions in a virtual environment or manage the workflow of multiple agents.