How can global games optimize multi-robot task allocation?
A Global Games-Inspired Approach to Multi-Robot Task Allocation for Heterogeneous Teams
This paper proposes a new algorithm for assigning tasks to multiple robots, especially when the robots have different capabilities and the tasks have changing priorities. It uses a "global game" approach, where each robot makes decisions based on shared signals representing the urgency and progress of each task.
For LLM-based multi-agent systems, the key takeaway is the concept of using a shared global signal to coordinate decentralized decision-making. This could be implemented by having LLMs representing agents observe a shared state (like the signals in the paper) and independently decide their actions, promoting scalable and robust coordination without direct communication. The paper's approach to handling heterogeneous agents (robots with varying capabilities) is also relevant, suggesting potential solutions for managing diverse LLM agents with specialized skills within a multi-agent application.