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^*

^*Corresponding author for this work

School of Information and Electronics

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings
Editors	Yuan Xu, Hongyang Yan, Huang Teng, Jun Cai, Jin Li
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	634-648
Number of pages	15
ISBN (Print)	9783031200953
DOIs	https://doi.org/10.1007/978-3-031-20096-0_48
Publication status	Published - 2023
Event	4th International Conference on Machine Learning for Cyber Security, ML4CS 2022 - Guangzhou, China Duration: 2 Dec 2022 → 4 Dec 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13655 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Conference on Machine Learning for Cyber Security, ML4CS 2022
Country/Territory	China
City	Guangzhou
Period	2/12/22 → 4/12/22

Keywords

Behavior modeling
Multi-agent system
Opponent modeling

Access to Document

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

Cite this

Liu, L., Yang, J., Zhang, Y., Zhang, J., & Ma, Y. (2023). An Overview of Opponent Modeling for Multi-agent Competition. In Y. Xu, H. Yan, H. Teng, J. Cai, & J. Li (Eds.), Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings (pp. 634-648). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 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 et al. / An Overview of Opponent Modeling for Multi-agent Competition. Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings. editor / Yuan Xu ; Hongyang Yan ; Huang Teng ; Jun Cai ; Jin Li. Springer Science and Business Media Deutschland GmbH, 2023. pp. 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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doi = "10.1007/978-3-031-20096-0_48",

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Liu, L, Yang, J, Zhang, Y, Zhang, J & Ma, Y 2023, An Overview of Opponent Modeling for Multi-agent Competition. in Y Xu, H Yan, H Teng, J Cai & J Li (eds), 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), vol. 13655 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 634-648, 4th International Conference on Machine Learning for Cyber Security, ML4CS 2022, Guangzhou, China, 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 et al.
Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings. ed. / Yuan Xu; Hongyang Yan; Huang Teng; Jun Cai; Jin Li. Springer Science and Business Media Deutschland GmbH, 2023. p. 634-648 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13655 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Yang, Jie

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AU - Zhang, Jingci

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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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A2 - Yan, Hongyang

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Y2 - 2 December 2022 through 4 December 2022

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Liu L, Yang J, Zhang Y, Zhang J, Ma Y. An Overview of Opponent Modeling for Multi-agent Competition. In Xu Y, Yan H, Teng H, Cai J, Li J, editors, Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 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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Keywords

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