As urbanization accelerates, cities become more complex, coming along with more complex urban issues. Agent‐based model (ABM) is a traditional method to simulate activities in a complex system, which has been widely applied in urban studies. However, due to its rigid initial settings, ABM has been criticized for its lack of intelligence, especially in dealing with modern urban issues. With the success of artificial intelligence (AI) and complexity science, it is generally agreed that ABM can be enhanced with AI agents, a promising technology that can bridge the gaps. For that, this article provides a systematic review, in which 10 subsections correspond to 10 different ways that AI can work with ABM in the methodological framework. The sections include that (1) ABM is Al; (2) ABM provides training data for Al; (3) Al provides data for ABM; (4) ABM is a submodule in the ensemble Al; (5) Al leads an optimization framework with ABM participation; (6) Al tunes ABM initialization parameters; (7) Al provides the environment for ABM; (8) Al aids in choosing the agent's attributes; (9) Al provides behaviors for agents in ABM; (10) Al helps to evaluate the performance of ABM. For each case, some typical works are examined for illustration. Finally, we discuss some of the current limitations and prospects for future development.

中文翻译：

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人工智能如何与基于主体的建模合作进行城市研究：系统回顾

随着城镇化进程加快，城市变得更加复杂，城市问题也更加复杂。基于主体的模型（ABM）是模拟复杂系统中活动的传统方法，已广泛应用于城市研究。然而，由于其僵化的初始设定，ABM一直被批评缺乏智能，尤其是在处理现代城市问题方面。随着人工智能 (AI) 和复杂性科学的成功，人们普遍认为可以通过 AI 代理来增强 ABM，这是一种可以弥补差距的有前景的技术。为此，本文提供了系统回顾，其中 10 个小节对应于人工智能在方法论框架中与 ABM 合作的 10 种不同方式。这些部分包括： (1) ABM 是 Al；（2）ABM为AI提供训练数据；(3) Al为ABM提供数据；(4) ABM是系综Al中的子模块；(5) Al主导ABM参与的优化框架；(6)Al调整ABM初始化参数；(7)Al为ABM提供环境；(8) Al帮助选择agent的属性；(9) Al为ABM中的Agent提供行为；(10)Al有助于评估ABM的性能。对于每个案例，都会检查一些典型作品来进行说明。最后，我们讨论了当前的一些局限性和未来发展的前景。