How to plan paths for multiple agents to find information?
Multi-Agent Vulcan: An Information-Driven Multi-Agent Path Finding Approach
September 23, 2024
https://arxiv.org/pdf/2409.13065This paper introduces "Multi-Agent Vulcan", a system where multiple AI agents collaborate to efficiently explore an environment and identify "phenomena of interest" within a limited timeframe.
Key points for LLM-based multi-agent systems:
- Information-driven approach: Agents use a reward function based on "mutual information gain" to prioritize exploration of unseen areas and minimize redundant observations.
- Decoupled heuristic: A computationally efficient heuristic based on single-agent exploration is used to guide the multi-agent planning process, reducing the need for complex reward calculations.
- Distributed execution: The system can operate in a distributed manner, with agents planning independently when outside communication range and collaboratively when within range.