Can LLMs power swarm intelligence agents?
MULTI-AGENT SYSTEMS POWERED BY LARGE LANGUAGE MODELS: APPLICATIONS IN SWARM INTELLIGENCE
March 7, 2025
https://arxiv.org/pdf/2503.03800This paper explores using Large Language Models (LLMs) to control agents in multi-agent simulations, specifically focusing on swarm intelligence examples like ant foraging and bird flocking. Instead of hard-coded rules, agent behaviors are determined by prompts given to the LLM.
Key points for LLM-based multi-agent systems:
- Prompt Engineering is Crucial: Carefully crafted prompts, refined through iterative testing, are essential for effective LLM control. Structured, rule-based prompts work well for tasks like ant foraging, while more general, principle-based prompts are suitable for emergent behaviors like flocking.
- LLMs Can Replicate Rule-Based Behavior: LLM-driven agents achieved comparable performance to rule-based agents in both foraging and flocking scenarios.
- Hybrid Systems Show Promise: Combining LLM-driven agents with traditional rule-based agents can lead to superior performance, leveraging the strengths of both approaches.
- Stateless vs. Stateful Agents: This research primarily used stateless prompts, requiring full context at each step. Future work could explore incorporating memory or state within the LLM for more complex scenarios.
- Computation Cost: Using LLMs introduces higher computational costs compared to simple rules, which could be mitigated by using smaller, locally hosted models.
- Toolchain for NetLogo: The paper provides a practical toolchain for integrating LLMs into NetLogo, a popular multi-agent simulation platform. This enables JavaScript developers familiar with NetLogo's JavaScript extension to experiment with LLM-driven agents.