Can sequential planning efficiently solve multi-agent problems?
Optimally Solving Simultaneous-Move Dec-POMDPs: The Sequential Central Planning Approach
This paper introduces a new approach to coordinating multiple AI agents, called sequential-move centralized training for decentralized execution. Instead of having all agents make decisions simultaneously, a central planner decides for each agent sequentially. This simplifies the planning process and significantly improves scalability for larger numbers of agents and longer planning horizons.
For LLM-based multi-agent systems, this research is particularly relevant as it offers a more tractable way to manage complex interactions between multiple LLMs. The sequential approach reduces the computational burden of coordinating LLMs, potentially improving efficiency and enabling the development of more sophisticated multi-agent applications. It also addresses the credit assignment problem by breaking down the overall task, facilitating individual LLM evaluation and improvement within the group.