Empirical Learning of Decision Parameters for Agent-Based Model
文献类型：会议论文
| 作者 | Song B(宋冰)1,4 ; Xiong G(熊刚)2,3,4; Zhu F（朱凤华）1,4; Wu X（武许可）1,4; Lv Y（吕宜生）1,4; Ye P（叶佩军）1,4
|
| 出版日期 | 2022 |
| 会议日期 | 2022 |
| 会议地点 | Macau, China |
| 英文摘要 | Agent-Based Model (ABM) is a widely used tool
to analyze distributed systems. However, the decision-making
parameters are difficult to determine, since ABM is a kind of
micro model and such parameters, varying from person to
person, cannot be measured conveniently in real traffic systems.
For this problem, this paper introduces reinforcement learning
to empirically and efficiently calculate the micro parameters of
ABM. By a parameterization of the individual interactions, our
new approach is able to decouple the dependence for a given
agent upon his “social neighbors”, and thus can accelerate the
learning process. Experiments on inter-city traveling of
population indicate that the proposed method is effective for the
micro parameter computation. |
| 会议录出版者 | IEEE |
| 源URL | [http://ir.ia.ac.cn/handle/173211/52159]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Ye P（叶佩军） |
| 作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences 2.The Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, China 3.The Cloud Computing Center, Chinese Academy of Sciences, Dongguan 523808, China 4.State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Song B,Xiong G,Zhu F,et al. Empirical Learning of Decision Parameters for Agent-Based Model[C]. 见:. Macau, China. 2022. |
入库方式： OAI收割
来源：自动化研究所
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