How can I efficiently allocate tasks among LLMs?
Distributed Task Allocation for Multi-Agent Systems: A Submodular Optimization Approach
This paper proposes a new algorithm, DGBA (Distributed Greedy Bundles Algorithm), for assigning tasks to multiple agents (e.g., robots, satellites) in a dynamic environment where resources are limited and tasks must be completed without conflicts. The goal is to maximize overall "utility" (i.e., how effectively tasks are completed).
Key points for LLM-based multi-agent systems: DGBA uses a "greedy" approach, where agents make locally optimal decisions based on limited information and negotiate with neighbors to resolve conflicts. This aligns with decentralized, communication-constrained LLM agents. The submodular optimization framework used ensures a good solution even with incomplete information, which is relevant for LLM agents relying on probabilistic outputs. The focus on dynamic environments and resource constraints mirrors the real-world scenarios where LLM-based multi-agent systems are deployed. The paper's application to satellite task allocation provides a concrete example of how the principles can be applied in complex, resource-limited multi-agent systems.