将大模型融入地理仿真，构建下一代智能模拟系统。
A survey of multi-agent geosimulation methodologies: from ABM to LLM
- 提出面向地理仿真的多智能体结构框架
- 验证大模型可作为感知-规划-行动组件有效集成
- 适合地理信息、城市模拟等领域的研究者
本文全面考察了基于代理的多智能体系统、仿真与信息系统的方法论。基于二十年的研究积累，提出一个正式的地理仿真平台规范框架。研究发现，若大语言模型（LLMs）遵循感知、记忆、规划和行动等基础代理行为的结构化架构，便可有效作为代理组件集成。该集成方式与所提出的架构高度一致，为下一代地理仿真系统提供了坚实基础。
原文摘要 · Abstract (English)
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.
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