Can vision predict multi-agent behavior?
LEARNING COLLECTIVE DYNAMICS OF MULTI-AGENT SYSTEMS USING EVENT-BASED VISION
November 12, 2024
https://arxiv.org/pdf/2411.07039This paper introduces evMAP, a new method using event-based vision (cameras detecting changes in brightness) to predict the collective behavior of large groups of interacting agents (like flocks of birds or robot swarms). It's designed to predict things like how strongly agents interact and when their behavior converges to a shared goal, directly from visual data without needing to track individual agents.
Key points for LLM-based multi-agent systems:
- Focus on Collective Behavior: evMAP predicts the group's behavior, not individual agent trajectories, which could be simpler for LLMs to manage in complex multi-agent scenarios.
- Event-Based Perception: Using event cameras offers advantages over traditional video for capturing dynamic interactions, offering potential efficiency gains for LLM processing.
- Real-time Prediction: evMAP is designed for real-time performance, important for responsive multi-agent systems controlled by LLMs.
- Adaptability to Dynamic Changes: evMAP handles changes in interaction dynamics better than some existing methods, which is relevant for LLM-based systems that need to adapt to evolving situations.
- Potential for LLM Integration: Though not explicitly explored, evMAP's outputs (interaction strength, convergence time) could be valuable inputs for LLMs coordinating or controlling multi-agent systems.