@inproceedings{96d9cfd47fcd483aa89a0fe429f5aeb7,
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",
year = "2023",
doi = "10.1007/978-3-031-20096-0\_48",
language = "English",
isbn = "9783031200953",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "634--648",
editor = "Yuan Xu and Hongyang Yan and Huang Teng and Jun Cai and Jin Li",
booktitle = "Machine Learning for Cyber Security - 4th International Conference, ML4CS 2022, Proceedings",
address = "Germany",
}