How to group similar AI agents for faster simulations?
ACCELERATING HYBRID AGENT-BASED MODELS AND FUZZY COGNITIVE MAPS: HOW TO COMBINE AGENTS WHO THINK ALIKE?
September 4, 2024
https://arxiv.org/pdf/2409.00824This paper explores methods for simplifying complex multi-agent simulations by grouping similar agents with "like-minded" behavior, represented by Fuzzy Cognitive Maps (FCMs), into single representative "super-agents." This reduces computational cost while maintaining simulation fidelity.
The key points relevant to LLM-based multi-agent systems are:
- FCMs as agent behavior models: FCMs, acting as individual "mental models," can represent the heterogeneous behavior of agents, similar to how LLMs could grant unique reasoning abilities to each agent.
- Similarity metrics for LLMs: This paper explores various metrics to compare FCMs, offering potential ways to evaluate the similarity between LLMs controlling different agents for grouping purposes.
- Simplified agents, complex systems: This work demonstrates a method to study complex systems with reduced computational burden, which is crucial for large-scale multi-agent simulations involving computationally expensive LLMs.