The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
A Decision-making Method for Swarm Agents in Attack-defense Confrontation
Open accessAbstract
The cooperative decision-making of swarm agents has attracted extensive attention from researchers due to its potential applications in multidisciplinary engineering problems. This paper studies a confrontation problem called asymmetric attack-defense confrontation (i.e., considering the difference in capability and quantity between agents and targets). The objective is to develop an effective self-organized swarm confrontation decision-making method. The decision-making process consists of task allocation decision and swarm motion decision. At each decision-making step, firstly, each agent forms a coalition with other agents autonomously by using a proposed hedonic coalition formation algorithm according to the attribute of targets. Thus, the agents assigned to the same target form a coalition, and swarm agents form several disjoint coalitions. Then, based on the allocated results, the agents are steered toward the corresponding target by a combat stimulus and a proposed selected interaction swarm algorithm. Finally, while the targets are within the agents’ attack radius, the agents execute the confrontation decision. Simulation results show the effectiveness of the designed method.
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In military and cybersecurity agentic AI systems, every agent can independently act based on its own preferences.
“Every agent can independently act based on its own preferences.”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [119]
In military and cybersecurity efforts, agentic AI makes it possible to have groups of autonomous agents carry out both offensive and defensive actions based on collective coordinated decisions.
“In military and cybersecurity efforts, agentic AI makes it possible to have groups of autonomous agents to carry out both offensive actions and defensive actions based on collective coordinated decisions.”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [119]
Military and cybersecurity agents can defend their positions, attack enemy positions, move around as needed, adapt their movements based on performance metrics, and choose to deploy a variety of effective formations.
“They can defend their positions, attack enemy positions, move around as needed, adapt their movements based on performance metrics, and choose to deploy a variety of effective formations [119].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [119]
Real-time data can involve coordinates or the accurate location of a person, car, or agent in a game, allowing the system to respond immediately and perform actions in a timely manner.
“Real-time data can also involve coordinates or the accurate location of a person, car, or agent in a game, allowing the system to respond immediately and perform actions timely manner [72,119].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [119]
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Metadata
- Authors
- Wang, L., Qiu, T., Pu, Z., Yi, J., Zhu, J., & Zhao, Y.
- Affiliations
- Chinese Academy of Sciences (CN)
- China Academy of Electronics and Information Technology (CN)
- Corresponding author
- Wang, Lexing
- Qiu, Tenghai
- Pu, Zhiqiang
- Yi, Jianqiang
- Zhu, Jinying
- Zhao, Yanjie
- Venue
- IFAC-PapersOnLine
- Publisher
- Elsevier BV
- Published
- 2023
- Volume
- 56
- Issue
- 2
- Pages
- 7858-7864
- ISSN
- 2405-8963 (ISSN-L)
- OpenAlex
- W4388904831
- Cited by
- 11
- References
- 19
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 1
- FWCI
- 1.22
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- IFAC-PapersOnLine (publishedVersion)
- Funders
- Chinese Academy of Sciences (XDA27030204)
- Beijing Nova Program (20220484077)
- National Key Research and Development Program of China (2018AAA0102405)
- National Natural Science Foundation of China (62073323)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Distributed Control Multi-Agent Systems
- Physical Sciences › Engineering › Aerospace Engineering › Military Defense Systems Analysis
- Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
- Keywords
- swarm agents, cooperative decision-making, task allocation, self-organization, multi-agent systems
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