How artificial intelligence cooperating with agent-based modeling for urban studies: A systematic review

IF 2.1 3区 地球科学 Q2 GEOGRAPHY Transactions in GIS Pub Date : 2024-03-04 DOI:10.1111/tgis.13152
Zijian Guo, Xintao Liu
{"title":"How artificial intelligence cooperating with agent-based modeling for urban studies: A systematic review","authors":"Zijian Guo, Xintao Liu","doi":"10.1111/tgis.13152","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions in GIS","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1111/tgis.13152","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOGRAPHY","Score":null,"Total":0}
引用次数: 0

Abstract

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
人工智能如何与基于代理的城市研究模型合作:系统回顾
随着城市化进程的加快,城市变得越来越复杂,随之而来的城市问题也越来越复杂。基于代理的模型(ABM)是一种模拟复杂系统活动的传统方法,已被广泛应用于城市研究。然而,由于其僵化的初始设置,ABM 因其缺乏智能性而饱受诟病,尤其是在处理现代城市问题时。随着人工智能(AI)和复杂性科学的成功,人们普遍认为,人工智能代理可以增强 ABM 的功能,这是一项很有前途的技术,可以缩小差距。为此,本文提供了一篇系统性综述,其中的 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。这些小节包括:(1)ABM 是 Al;(2)ABM 为 Al 提供训练数据;(3)Al 为 ABM 提供数据;(4)ABM 是集合 Al 中的一个子模块;(5)Al 主导一个有 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 帮助选择代理的属性;(9)Al 为 ABM 中的代理提供行为;(10)Al 帮助评估 ABM 的性能。针对每种情况,我们都会研究一些典型作品,以作说明。最后,我们讨论了当前的一些局限性和未来的发展前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Transactions in GIS
Transactions in GIS GEOGRAPHY-
CiteScore
4.60
自引率
8.30%
发文量
116
期刊介绍: Transactions in GIS is an international journal which provides a forum for high quality, original research articles, review articles, short notes and book reviews that focus on: - practical and theoretical issues influencing the development of GIS - the collection, analysis, modelling, interpretation and display of spatial data within GIS - the connections between GIS and related technologies - new GIS applications which help to solve problems affecting the natural or built environments, or business
期刊最新文献
Knowledge‐Guided Automated Cartographic Generalization Process Construction: A Case Study Based on Map Analysis of Public Maps of China City Influence Network: Mining and Analyzing the Influence of Chinese Cities Based on Social Media PyGRF: An Improved Python Geographical Random Forest Model and Case Studies in Public Health and Natural Disasters Neural Sensing: Toward a New Approach to Understanding Emotional Responses to Place Construction of Earth Observation Knowledge Hub Based on Knowledge Graph
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1