How can I learn hidden interactions in real-time multi-agent systems?
Online Relational Inference for Evolving Multi-agent Interacting Systems
November 5, 2024
https://arxiv.org/pdf/2411.01442This paper introduces Online Relational Inference (ORI), a new method for identifying the hidden relationships between agents in a multi-agent system as it changes over time, using only the agents' observable actions (trajectories). It's like figuring out how different parts of a machine interact just by watching the machine operate, even if the parts start interacting differently over time.
Key points for LLM-based multi-agent systems:
- Adaptability to changing environments: ORI is designed to adapt to evolving agent interactions, unlike existing offline methods. This is crucial for real-world multi-agent systems where relationships aren't static.
- Real-time processing of streaming data: ORI updates its understanding of agent relationships with each new piece of data, making it suitable for real-time applications.
- Model-agnostic approach: ORI can be integrated with various existing multi-agent models (including those using LLMs), offering flexibility in architecture.
- Adaptive learning rate: ORI introduces "AdaRelation," a technique to dynamically adjust the learning rate based on how agent interactions are changing, crucial for stable and efficient learning in dynamic environments.
- Data augmentation: "Trajectory Mirror" enhances model generalization by creating variations in the observed data, improving the ability to identify correct relationships regardless of the perspective from which agents' actions are observed.
- Potential for improved interpretability: While primarily focused on accurate relationship inference, initial results suggest ORI might offer better interpretability compared to some existing methods. This could be valuable for understanding why LLMs in a multi-agent system make certain decisions.