How can I reliably control stochastic multi-agent systems with probabilistic guarantees?
Conformal Data-driven Control of Stochastic Multi-Agent Systems under Collaborative Signal Temporal Logic Specifications
This paper proposes a new method for controlling a group of AI agents (multi-agent system) in uncertain environments, ensuring they achieve collaborative goals expressed in formal logic (Signal Temporal Logic or STL). It utilizes a data-driven approach called Conformal Prediction (CP) to handle uncertainties without needing to know their exact distribution. This is done by learning "prediction regions" for possible errors in the agents' actions based on historical data, and then adjusting the agents' plans to stay within these safe regions.
Relevant to LLM-based multi-agent systems, CP offers a distribution-free way to manage uncertainty stemming from the stochastic nature of LLMs. The proposed distributed control synthesis approach allows agents to coordinate locally within subgroups (cliques), which could improve scalability in complex LLM-based multi-agent applications. This allows for tighter control of LLM behavior within a multi-agent setting, even when the exact probabilities of LLM outputs are unknown.