How to estimate state with limited communication in dynamic agent networks?
Decentralized Input and State Estimation for Multi-agent System with Dynamic Topology and Heterogeneous Sensor Network
October 2, 2024
https://arxiv.org/pdf/2410.00272This paper presents a novel algorithm for decentralized input and state estimation in multi-agent AI systems, particularly useful for scenarios like sensor networks. It allows agents to achieve near-optimal estimations by exchanging only intermediate estimations with their direct neighbors, instead of requiring full knowledge of the entire system or raw data from all agents.
This approach is relevant to LLM-based multi-agent systems as it offers:
- Privacy: Agents don't expose raw data or internal model parameters, preserving privacy.
- Scalability: The algorithm performs efficiently in dynamic networks with limited communication bandwidth.
- Robustness: It handles intermittent observations and unknown inputs affecting system dynamics.