How to regulate multi-agent systems without knowing their network?
Data-Driven Cooperative Output Regulation of Continuous-Time Multi-Agent Systems with Unknown Network Topology
This paper addresses the challenge of controlling multiple AI agents working together (cooperative output regulation) when the connections between them are unknown, using real-time data instead of perfect system models.
For LLM-based multi-agent systems, this research is particularly relevant as it tackles the problem of coordinating agents in dynamic environments where communication pathways might be uncertain. The proposed method relies solely on observed agent data to design control mechanisms, eliminating the need for explicit communication topology or individual agent model knowledge. This could be crucial in scenarios with LLMs acting as independent agents where pre-defining communication structures might be impractical.