Modeling agent decision and behavior in the light of data science and artificial intelligence

被引：17

作者：

An, Li ^{[1
,2
]}

Grimm, Volker ^{[3
]}

Bai, Yu ^{[4
]}

Sullivan, Abigail ^{[5
]}

Turner II, B. L. ^{[6
]}

Malleson, Nicolas ^{[7
]}

Heppenstall, Alison ^{[8
]}

Vincenot, Christian ^{[9
]}

Robinson, Derek ^{[10
]}

Ye, Xinyue ^{[11
]}

Liu, Jianguo ^{[11
,12
]}

Lindkvist, Emilie ^{[12
,13
]}

Tang, Wenwu ^{[13
,14
]}

机构：

[1] San Diego State Univ, Ctr Complex Human Environm Syst, 5500 Campanile Dr, San Diego, CA 92182 USA

[2] San Diego State Univ, Dept Geog, 5500 Campanile Dr, San Diego, CA 92182 USA

[3] UFZ Helmholtz Ctr Environm Res, Dept Ecol Modelling, Permoserstr 15, D-04318 Leipzig, Germany

[4] Calif State Univ Fullerton, Coll Engn & Comp Sci, Comp Engn Program, Fullerton, CA USA

[5] Boston Univ, Dept Earth & Environm, 685 Commonwealth Ave, Boston, MA 02215 USA

[6] Arizona State Univ, Sch Geog Sci & Urban Planning, Sch Sustainabil, POB 875302, Tempe, AZ 85287 USA

[7] Alan Turing Inst, British Lib, 96 Euston Rd, London NW1 2DB, England

[8] Univ Leeds, Ctr Spatial Anal & Policy, Sch Geog, Woodhouse Lane, Leeds LS2 9JT, England

[9] Univ Luxembourg, Fac Sci Technol & Med, Esch Sur Alzette, Luxembourg

[10] Univ Waterloo, Dept Geog & Environm Management, 200 Univ Ave West, Waterloo, ON N2L 3G1, Canada

[11] Texas A&M Univ, Dept Landscape Architecture & Urban Planning, College Stn, TX 77843 USA

[12] Michigan State Univ, Ctr Syst Integrat & Sustainabil, E Lansing, MI 48823 USA

[13] Stockholm Univ, Stockholm Resilience Ctr, Kraftriket 2B, S-10691 Stockholm, Sweden

[14] Univ N Carolina, Dept Geog & Earth Sci, 9201 Univ City Blvd, Charlotte, NC 28223 USA

来源：

ENVIRONMENTAL MODELLING & SOFTWARE | 2023年 / 166卷

基金：

欧洲研究理事会; 美国国家科学基金会;

关键词：

Agent-based modeling; Modeling agent decisions and actions; Artificial intelligence; Machine learning; Data science; CHALLENGES; NETWORKS;

D O I：

10.1016/j.envsoft.2023.105713

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Agent-based modeling (ABM) has been widely used in numerous disciplines and practice domains, subject to many eulogies and criticisms. This article presents key advances and challenges in agent-based modeling over the last two decades and shows that understanding agents' behaviors is a major priority for various research fields. We demonstrate that artificial intelligence and data science will likely generate revolutionary impacts for science and technology towards understanding agent decisions and behaviors in complex systems. We propose an innovative approach that leverages reinforcement learning and convolutional neural networks to equip agents with the intelligence of self-learning their behavior rules directly from data. We call for further developments of ABM, especially modeling agent behaviors, in the light of data science and artificial intelligence.

引用

页数：6

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