Can I infer NDS control from discrete observations?
Inverse Inference on Cooperative Control of Networked Dynamical Systems
This paper proposes a method to infer the "rules" of a multi-agent system by observing its behavior. Imagine observing a flock of birds and trying to figure out how each bird decides where to fly based solely on their positions over time. This method aims to do just that for systems controlled by algorithms. The method focuses on continuous-time, linear systems, meaning the bird's position smoothly evolves over time and responds proportionally to the other bird's influences. The method is broken into two levels: first estimating a simplified representation of the overall system from discrete snapshots, and then breaking this representation down to reveal individual agent behavior and their interactions. It also tries to reconstruct the underlying "goal" the agents are optimizing for.
Key points for LLM-based multi-agent systems: This work tackles the challenge of understanding agent interactions solely through observations, which is relevant for analyzing and debugging complex LLM-agent systems where internal decision-making processes may be opaque. The emphasis on continuous-time and linear approximations allows for the application of well-established mathematical tools, which might provide a starting point for analyzing more complex, non-linear LLM-agent behaviors. The ability to infer an objective function could be crucial for aligning LLM agents with developer intentions and ensuring desired emergent behaviors.