Integrating Large Language Models as Cognitive Agents into the GAMA Platform for Urban Mobility Simulation

Abstract

Urban mobility modeling presents challenges related to the complexity of individual behaviors in dynamic environments. Although Multi-agent Systems are used to simulate processes, rule-based approaches exhibit limitations in terms of adaptability and behavior. Based on recent advances in Large Language Models (LLMs), this work investigates their use as cognitive mechanisms to support agent decision-making. This study proposes the integration of LLM-based AI agents, implemented in Agno, into a spatial Multi-agent System developed in GAMA, for urban mobility simulation. The main contribution of this work is to present an architecture that extends a rule-based model in GAMA, incorporating context-sensitive decisions that consider individual aspects as well as environmental and temporal variables, enabled through an intermediate API that supports language-driven decision-making and persistent memory. Also, we conduct a comparative analysis between rule-based and LLM-assisted modeling. The results indicate that the LLM-assisted approach promotes greater behavioral diversity and increased context sensitivity in mobility decisions, as evidenced by the notable increase in the entropy of daily schedules. Despite the computational cost, the results suggest that the proposed approach represents a promising alternative for modeling complex behaviors in urban simulations.