How can agents collaboratively track targets in a decentralized system?
Decentralized Mobile Target Tracking Using Consensus-Based Estimation with Nearly-Constant-Velocity Modeling
This paper proposes a decentralized method for multiple agents (e.g., robots, sensors) to track a moving target. Each agent makes local observations and shares information with its neighbors to reach a consensus on the target's location. The system uses a nearly-constant-velocity model for target movement and a saturation-based filter to handle noisy sensor data.
For LLM-based multi-agent systems, the key takeaway is the consensus mechanism. The paper shows how agents can collaboratively build a shared understanding (the target's location) from individual, potentially incomplete perspectives. This is analogous to how LLMs in a multi-agent system could combine knowledge and reason collectively. The saturation-based filtering offers a way to handle the inherent uncertainty and potential hallucinations in LLM outputs, improving the system's robustness.