Empirical Learning of Decision Parameters for Agent-Based Model

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Empirical Learning of Decision Parameters for Agent-Based Model
	Song B(宋冰)1,2 ; Xiong G(熊刚)1,3,4 ; Zhu F（朱凤华）1,2 ; Wu X（武许可）1,2; Lv Y（吕宜生）1,2 ; Ye P（叶佩军）1,2
	2022
会议名称	2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
会议日期	2022
会议地点	Macau, China
出版者	IEEE
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.
七大方向——子方向分类	多智能体系统
国重实验室规划方向分类	多智能体决策
是否有论文关联数据集需要存交	否
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/52159
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Ye P（叶佩军）
作者单位	1.State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.The Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, China 4.The Cloud Computing Center, Chinese Academy of Sciences, Dongguan 523808, China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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]:IEEE,2022.

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