An Overview of Opponent Modeling for Multi-agent Competition

Lu Liu; Jie Yang; Yaoyuan Zhang; Jingci Zhang; Yuxi Ma

doi:10.1007/978-3-031-20096-0_48

An Overview of Opponent Modeling for Multi-agent Competition

Lu Liu, Jie Yang, Yaoyuan Zhang, Jingci Zhang, Yuxi Ma^*

^*此作品的通讯作者

信息与电子学院

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

Multi-agent system (MAS) is an area of distributed artificial intelligence that emphasizes the joint behaviors of agents with some degree of autonomy and the complexities arising from their interactions. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Opponent modeling is generally used in competitive multi-agent systems, in which an agent models the actions, behaviors, and strategies of other agents (adversaries) to get better rewards and train stronger strategies for playing against each other. In this survey, we give an overview of multi-agent learning research in a spectrum of areas, including reinforcement learning, evolutionary computation, game theory, and agent modeling.

源语言	英语
主期刊名	Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings
编辑	Yuan Xu, Hongyang Yan, Huang Teng, Jun Cai, Jin Li
出版商	Springer Science and Business Media Deutschland GmbH
页	634-648
页数	15
ISBN（印刷版）	9783031200953
DOI	https://doi.org/10.1007/978-3-031-20096-0_48
出版状态	已出版 - 2023
活动	4th International Conference on Machine Learning for Cyber Security, ML4CS 2022 - Guangzhou, 中国期限: 2 12月 2022 → 4 12月 2022

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	13655 LNCS
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	4th International Conference on Machine Learning for Cyber Security, ML4CS 2022
国家/地区	中国
市	Guangzhou
时期	2/12/22 → 4/12/22

访问文件

10.1007/978-3-031-20096-0_48

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引用此

Liu, L., Yang, J., Zhang, Y., Zhang, J., & Ma, Y. (2023). An Overview of Opponent Modeling for Multi-agent Competition. 在 Y. Xu, H. Yan, H. Teng, J. Cai, & J. Li (编辑), Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings (页码 634-648). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 13655 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-20096-0_48

Liu, Lu ; Yang, Jie ; Zhang, Yaoyuan 等. / An Overview of Opponent Modeling for Multi-agent Competition. Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings. 编辑 / Yuan Xu ; Hongyang Yan ; Huang Teng ; Jun Cai ; Jin Li. Springer Science and Business Media Deutschland GmbH, 2023. 页码 634-648 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "An Overview of Opponent Modeling for Multi-agent Competition",

abstract = "Multi-agent system (MAS) is an area of distributed artificial intelligence that emphasizes the joint behaviors of agents with some degree of autonomy and the complexities arising from their interactions. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Opponent modeling is generally used in competitive multi-agent systems, in which an agent models the actions, behaviors, and strategies of other agents (adversaries) to get better rewards and train stronger strategies for playing against each other. In this survey, we give an overview of multi-agent learning research in a spectrum of areas, including reinforcement learning, evolutionary computation, game theory, and agent modeling.",

keywords = "Behavior modeling, Multi-agent system, Opponent modeling",

author = "Lu Liu and Jie Yang and Yaoyuan Zhang and Jingci Zhang and Yuxi Ma",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 4th International Conference on Machine Learning for Cyber Security, ML4CS 2022 ; Conference date: 02-12-2022 Through 04-12-2022",

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Liu, L, Yang, J, Zhang, Y, Zhang, J & Ma, Y 2023, An Overview of Opponent Modeling for Multi-agent Competition. 在 Y Xu, H Yan, H Teng, J Cai & J Li (编辑), Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 卷 13655 LNCS, Springer Science and Business Media Deutschland GmbH, 页码 634-648, 4th International Conference on Machine Learning for Cyber Security, ML4CS 2022, Guangzhou, 中国, 2/12/22. https://doi.org/10.1007/978-3-031-20096-0_48

An Overview of Opponent Modeling for Multi-agent Competition. / Liu, Lu; Yang, Jie; Zhang, Yaoyuan 等.
Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings. 编辑 / Yuan Xu; Hongyang Yan; Huang Teng; Jun Cai; Jin Li. Springer Science and Business Media Deutschland GmbH, 2023. 页码 634-648 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 13655 LNCS).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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AU - Liu, Lu

AU - Yang, Jie

AU - Zhang, Yaoyuan

AU - Zhang, Jingci

AU - Ma, Yuxi

PY - 2023

Y1 - 2023

N2 - Multi-agent system (MAS) is an area of distributed artificial intelligence that emphasizes the joint behaviors of agents with some degree of autonomy and the complexities arising from their interactions. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Opponent modeling is generally used in competitive multi-agent systems, in which an agent models the actions, behaviors, and strategies of other agents (adversaries) to get better rewards and train stronger strategies for playing against each other. In this survey, we give an overview of multi-agent learning research in a spectrum of areas, including reinforcement learning, evolutionary computation, game theory, and agent modeling.

AB - Multi-agent system (MAS) is an area of distributed artificial intelligence that emphasizes the joint behaviors of agents with some degree of autonomy and the complexities arising from their interactions. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Opponent modeling is generally used in competitive multi-agent systems, in which an agent models the actions, behaviors, and strategies of other agents (adversaries) to get better rewards and train stronger strategies for playing against each other. In this survey, we give an overview of multi-agent learning research in a spectrum of areas, including reinforcement learning, evolutionary computation, game theory, and agent modeling.

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KW - Multi-agent system

KW - Opponent modeling

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Y2 - 2 December 2022 through 4 December 2022

ER -

Liu L, Yang J, Zhang Y, Zhang J, Ma Y. An Overview of Opponent Modeling for Multi-agent Competition. 在 Xu Y, Yan H, Teng H, Cai J, Li J, 编辑, Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. 页码 634-648. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-20096-0_48

An Overview of Opponent Modeling for Multi-agent Competition

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