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Electronics
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AI Applications of Multi-Agent Systems
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AI Applications of Multi-Agent Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 September 2025) | Viewed by 3657

Special Issue Editors

Dr. Wanyuan Wang

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Interests: artificial intelligence; multi-agent systems; reinforcement learning; large-language model-driven decision making
Dr. Yifeng Zhou

E-Mail Website
Guest Editor
School of Computer Science, Nanjing Audit University, Nanjing 211815, China
Interests: multiagent systems; AI for education
Dr. Vincent Chau

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Interests: algorithms design and analysis; power-aware scheduling problems; combinatorial optimization

Special Issue Information

Dear Colleagues,

With the rapid development of information technology and robotics, multiagent systems (MAS), due to their strong fault tolerance, robustness, and scalability, have become an effective method that can model, analyze, and compute autonomous multi-subject systems, now widely used in traffic light signal control, unmanned driving, smart warehousing and logistics, UAV collaboration, and emerging large language model planning and collaboration. The aim of the Special Issue is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems.

We welcome the submission of technical papers describing significant and original research on all aspects of the theory and practice of autonomous agents and multiagent systems. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Coordination, organizations, institutions, norms, and ethics;
  • Engineering multiagent systems;
  • Humans and AI/human–agent interaction;
  • Innovative applications, in particular addressing the sustainable development goals;
  • Knowledge representation, reasoning, and planning;
  • Learning and adaptation;
  • Markets, auctions, and non-cooperative game theory;
  • Modeling and simulation of societies;
  • (Multiagent) Reinforcement learning;
  • Robotics;
  • Large language model agents.

We look forward to receiving your contributions. 

Dr. Wanyuan Wang
Dr. Yifeng Zhou
Dr. Vincent Chau
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multiagent systems
  • large language model agents
  • reinforcement learning
  • planning
  • machine Learning

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Published Papers (4 papers)

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Research

48 pages, 15781 KB  
Article
Autonomous AI Agents for Multi-Platform Social Media Marketing: A Simultaneous Deployment Study
by Joongho Ahn and Moonsoo Kim
Electronics 2025, 14(21), 4161; https://doi.org/10.3390/electronics14214161 - 24 Oct 2025
Abstract
This exploratory proof-of-concept study investigated the simultaneous deployment of autonomous, persona-driven Artificial Intelligence (AI) agents across multiple social media platforms using the ElizaOS framework. We developed three platform-specific agents with seven-layer character architectures and deployed them on Twitter/X, Discord, and Telegram for 18 [...] Read more.
This exploratory proof-of-concept study investigated the simultaneous deployment of autonomous, persona-driven Artificial Intelligence (AI) agents across multiple social media platforms using the ElizaOS framework. We developed three platform-specific agents with seven-layer character architectures and deployed them on Twitter/X, Discord, and Telegram for 18 days. The system processed 5389 interactions while gathering feedback from 28 volunteer participants. Addressing three research questions, we found that: (1) automation effectiveness was platform-dependent, with direct support platforms (Telegram, Discord) rated more useful than broadcast-oriented Twitter/X; (2) character design impact depended primarily on platform-persona alignment rather than architectural sophistication; and (3) technical performance showed platform-specific patterns, with median storage times ranging from 9.0 milliseconds (Twitter/X) to 61.5 milliseconds (Telegram) and high variability across all platforms. A notable finding was what we term the “Discord Paradox”—high quality ratings (4.05/5) but lowest preference (8.7%), suggesting platform familiarity and accessibility influence adoption more than agent quality. While the deployment demonstrated technical feasibility and revealed distinct user dynamics across platforms, the findings indicate that platform-specific optimization may be more effective than universal approaches. This exploratory study advances understanding of multi-platform agent deployment for marketing automation, identifying behavioral patterns and platform-specific dynamics that offer testable hypotheses for future systematic research. Full article
(This article belongs to the Special Issue AI Applications of Multi-Agent Systems)
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28 pages, 2797 KB  
Article
TTBCA+: An Enhanced Besiege and Conquer Algorithm with Three-Three Tactics and Collision Theory for Complex Optimization Problems
by Jianhua Jiang, Xiangyu Xin, Jun Tian and Hao Li
Electronics 2025, 14(20), 4051; https://doi.org/10.3390/electronics14204051 - 15 Oct 2025
Viewed by 230
Abstract
Besiege and Conquer Algorithm (BCA) is a swarm intelligence algorithm proposed in 2025 based on tactical concepts. However, its besiege and conquer strategies have some problems, such as insufficient diversity and local stagnation. To solve the above problems, this paper proposes an Enhanced [...] Read more.
Besiege and Conquer Algorithm (BCA) is a swarm intelligence algorithm proposed in 2025 based on tactical concepts. However, its besiege and conquer strategies have some problems, such as insufficient diversity and local stagnation. To solve the above problems, this paper proposes an Enhanced Besiege and Conquer Algorithm with Three-Three Tactics and Collision Theory, referred to as TTBCA+. TTBCA+ has four innovations. Firstly, a three-three allocation mechanism of battlefield and roles is proposed to enhance its besiege capability; secondly, a three-three deployment mechanism of soldiers is proposed to enhance its conquer capability; thirdly, the balance factor between exploration and exploitation is modified for three-three tactics implementation; finally, the collision mechanism from collision theory is introduced to deal with soldiers beyond the search space. The performance of the proposed TTBCA+ is verified at the IEEE CEC 2017 and IEEE CEC 2022 benchmark functions, compared with 13 swarm intelligence algorithms, including classical algorithms, well-known algorithms such as JADE, lshade_rsp, AGWO, and recent 5 years algorithms, such as BCA, HOA, PLO, CFOA, HLOA, DBO, BOA. Meanwhile, the proposed algorithms are applied to two practical complex optimization problems. The results show that TTBCA+ effectively solved the limitations in BCA, and it is superior to other compared algorithms. Full article
(This article belongs to the Special Issue AI Applications of Multi-Agent Systems)
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18 pages, 3227 KB  
Article
Optimized Adversarial Tactics for Disrupting Cooperative Multi-Agent Reinforcement Learning
by Guangze Yang, Xinyuan Miao, Yabin Peng, Wei Huang and Fan Zhang
Electronics 2025, 14(14), 2777; https://doi.org/10.3390/electronics14142777 - 10 Jul 2025
Viewed by 857
Abstract
Multi-agent reinforcement learning has demonstrated excellent performance in complex decision-making tasks such as electronic games, power grid management, and autonomous driving. However, its vulnerability to adversarial attacks may impede its widespread application. Currently, research on adversarial attacks in reinforcement learning primarily focuses on [...] Read more.
Multi-agent reinforcement learning has demonstrated excellent performance in complex decision-making tasks such as electronic games, power grid management, and autonomous driving. However, its vulnerability to adversarial attacks may impede its widespread application. Currently, research on adversarial attacks in reinforcement learning primarily focuses on single-agent scenarios, while studies in multi-agent settings are relatively limited, especially regarding how to achieve optimized attacks with fewer steps. This paper aims to bridge the gap by proposing a heuristic exploration-based attack method named the Search for Key steps and Key agents Attack (SKKA). Unlike previous studies that train a reinforcement learning model to explore attack strategies, our approach relies on a constructed predictive model and a T-value function to search for the optimal attack strategy. The predictive model predicts the environment and agent states after executing the current attack for a certain period, based on simulated environment feedback. The T-value function is then used to evaluate the effectiveness of the current attack. We select the strategy with the highest attack effectiveness from all possible attacks and execute it in the real environment. Experimental results demonstrate that our attack method ensures maximum attack effectiveness while greatly reducing the number of attack steps, thereby improving attack efficiency. In the StarCraft Multi-Agent Challenge (SMAC) scenario, by attacking 5–15% of the time steps, we can reduce the win rate from 99% to nearly 0%. By attacking approximately 20% of the agents and 24% of the time steps, we can reduce the win rate to around 3%. Full article
(This article belongs to the Special Issue AI Applications of Multi-Agent Systems)
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21 pages, 4319 KB  
Article
Research on Real-Time Multi-Robot Task Allocation Method Based on Monte Carlo Tree Search
by Huiying Zhang, Yule Sun and Fengzhi Zheng
Electronics 2024, 13(24), 4943; https://doi.org/10.3390/electronics13244943 - 15 Dec 2024
Viewed by 1560
Abstract
Task allocation is an important problem in multi-robot systems, particularly in dynamic and unpredictable environments such as offshore oil platforms, large-scale factories, or disaster response scenarios, where high change rates, uncertain state transitions, and varying task demands challenge the predictability and stability of [...] Read more.
Task allocation is an important problem in multi-robot systems, particularly in dynamic and unpredictable environments such as offshore oil platforms, large-scale factories, or disaster response scenarios, where high change rates, uncertain state transitions, and varying task demands challenge the predictability and stability of robot operations. Traditional static task allocation strategies often struggle to meet the efficiency and responsiveness demands of these complex settings, while optimization heuristics, though improving planning time, exhibit limited scalability. To address these limitations, this paper proposes a task allocation method based on the Monte Carlo Tree Search (MCTS) algorithm, which leverages the anytime property of MCTS to achieve a balance between fast response and continuous optimization. Firstly, the centralized adaptive MCTS algorithm generates preliminary solutions and monitors the state of the robots in minimal time. It utilizes dynamic Upper Confidence Bounds for Trees (UCT) values to accommodate varying task dimensions, outperforming the heuristic Multi-Robot Goal Assignment (MRGA) method in both planning time and overall task completion time. Furthermore, the parallelized distributed MCTS algorithm reduces algorithmic complexity and enhances computational efficiency through importance sampling and parallel processing. Experimental results demonstrate that the proposed method significantly reduces computation time while maintaining task allocation performance, decreasing the variance of planning results and improving algorithmic stability. Our approach enables more flexible and efficient task allocation in dynamically evolving and complex environments, providing robust support for the deployment of multi-robot systems. Full article
(This article belongs to the Special Issue AI Applications of Multi-Agent Systems)
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