A survey of multi-agent geosimulation methodologies: from ABM to LLM
This work provides a formal specification for next-generation geosimulation platforms, benefiting researchers and practitioners in simulation and information systems, though it is incremental as it builds on existing methodologies.
The paper tackles the integration of large language models (LLMs) into multi-agent geosimulation systems by formalizing a framework based on agent-based approaches, showing that LLMs can effectively serve as agent components when aligned with a structured architecture for activities like perception and planning.
We provide a comprehensive examination of agent-based approaches that codify the principles and linkages underlying multi-agent systems, simulations, and information systems. Based on two decades of study, this paper confirms a framework intended as a formal specification for geosimulation platforms. Our findings show that large language models (LLMs) can be effectively incorporated as agent components if they follow a structured architecture specific to fundamental agent activities such as perception, memory, planning, and action. This integration is precisely consistent with the architecture that we formalize, providing a solid platform for next-generation geosimulation systems.