How to safely coordinate AI agents for real-time control?
Synchronization-Based Cooperative Distributed Model Predictive Control
September 17, 2024
https://arxiv.org/pdf/2409.10215The research focuses on solving the problem of inconsistent predictions in distributed control systems, specifically for cooperative multi-agent systems like self-driving cars. The paper proposes an algorithm called Synchronization-Based Cooperative Distributed Model Predictive Control (SCDMPC) that uses an iterative synchronization process, inspired by multi-agent consensus, to ensure all agents agree on predicted states and actions.
Key takeaways for LLM-based multi-agent systems:
- Prediction consistency is crucial: In multi-agent systems where agents make decisions based on predictions of others, inconsistent predictions can lead to failures.
- Synchronization as a solution: Iterative synchronization mechanisms can help agents achieve a shared understanding of the world and make consistent predictions.
- Scalability for large systems: The proposed approach maintains communication efficiency by limiting communication to neighboring agents, making it potentially suitable for LLM-based systems.