How can I control agent spatial behavior in LLMs?
MODELLING AND CONTROL OF SPATIAL BEHAVIOURS IN MULTI-AGENT SYSTEMS WITH APPLICATIONS TO BIOLOGY AND ROBOTICS
January 3, 2025
https://arxiv.org/pdf/2501.00110This PhD thesis investigates the modeling and control of spatial behaviors in multi-agent systems, with applications in swarm robotics and the control of microorganisms. The author develops a distributed control algorithm for robots to form geometric patterns (like lattices) and uses a data-driven approach to model the motion and light response of microorganisms, aiming for spatial control.
Key points for LLM-based multi-agent systems:
- Formal methods: The thesis uses formal analysis (graph theory, Lyapunov stability) to prove the stability of robot formations, which could inspire similar rigorous approaches to verify the emergent behavior of LLM-based agents.
- Data-driven modeling: The data-driven approach used to model microorganism behavior offers a potential pathway for modeling complex interactions and emergent behaviors in LLM-agent systems where first-principles models are difficult to construct.
- Spatiotemporal control: The concept of controlling agent density and behavior in both space and time, explored with microorganisms, could translate to managing and coordinating large numbers of LLM agents in virtual or physical environments.
- Hierarchical control: The thesis proposes a hierarchical control architecture (high-level task specification to individual agent control) which could be valuable for structuring complex LLM-based multi-agent applications.
- SwarmSim: The author develops SwarmSim, a multi-agent simulation framework that could be extended to incorporate LLM-based agent behaviors and explore emergent properties in simulated environments.