A survey of multi-agent geosimulation methodologies: from ABM to LLM
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908473955975168 |
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| author | Padilla, Virginia Dávila, Jacinto |
| author_facet | Padilla, Virginia Dávila, Jacinto |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_23694 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A survey of multi-agent geosimulation methodologies: from ABM to LLM Padilla, Virginia Dávila, Jacinto Multiagent Systems Artificial Intelligence 68T42 I.2.11 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. |
| title | A survey of multi-agent geosimulation methodologies: from ABM to LLM |
| topic | Multiagent Systems Artificial Intelligence 68T42 I.2.11 |
| url | https://arxiv.org/abs/2507.23694 |