Submission Deadline: 30 September 2025 View: 245 Submit to Special Issue
Prof. Wen-Jer Chang
Email: wjchang@mail.ntou.edu.tw; wjchangntou@gmail.com
Affiliation: Department of Marine Engineering, National Taiwan Ocean University, Keelung, 20224, Taiwan.
Research Interests: Intelligent control, Fuzzy control, Robust control, Multi-Agent system control
Prof. Muhammad Shamrooz Aslam
Email: shamroz_aslam@cumt.edu.cn
Affiliation: Artificial Intelligence Reserach Institute, China University of Mining and Technology, Xuzhou, 221008, China.
Research Interests: Multi-Agent systems; Supply chain management in Control system; T-S fuzzy system; Networked Control system
Prof. Yi-Chen Lee
Email: yeleeim@gms.ndhu.edu.tw
Affiliation: Department of Information Management, National Dong Hwa University, Hualien, 974301, Taiwan.
Research Interests: Human-computer interaction and user cognition, Information Systems and website assessment, Knowledge management, Fuzzy theory and fuzzy decision-making, Multi-Agent system control
Multi-agent systems (MAS) are computational frameworks comprising multiple autonomous agents that interact and collaborate to achieve individual or collective goals. Over the years, MAS have gained significant traction in control engineering and information science due to their inherent scalability, adaptability, and resilience. These systems excel in solving complex, distributed problems in dynamic environments where traditional centralized approaches often fail. In control engineering, MAS provides robust solutions for distributed control, synchronization, and optimization tasks across industries such as energy, transportation, and robotics. They empower systems like smart grids, autonomous vehicles, and industrial automation to function cohesively, even under uncertain and rapidly changing conditions. Similarly, in information science, MAS enables intelligent data processing, decision-making, and simulation across domains, including social networks, IoT, and cybersecurity. The integration of advanced computational models such as machine learning, game theory, and reinforcement learning into MAS has further enhanced their capabilities. These computational methods allow agents to learn, adapt, and make intelligent decisions in real-time, making MAS a cornerstone for innovation in intelligent systems. This Special Issue aims to explore innovative computational models and practical applications of MAS in control engineering and information science. Highlighting the intersection of these fields will provide a platform for researchers and practitioners to present cutting-edge advancements, foster interdisciplinary collaboration, and pave the way for future developments in this dynamic area.
This special issue aims to bring together cutting-edge research and innovative applications of multi-agent systems (MAS) in control engineering and information science. The focus is to explore how MAS can address complex, dynamic, and distributed challenges in these fields. The issue seeks to advance the theoretical foundations of MAS while emphasizing practical implementations that demonstrate their transformative potential across various domains. By fostering interdisciplinary collaboration, this special issue intends to provide a comprehensive understanding of MAS and its applications, promoting advancements that bridge theory and practice.
The scope of this special issue spans the design, analysis, and application of MAS in solving problems in control engineering and information science. The issue invites contributions that address novel methodologies, theoretical developments, and practical implementations.
Specific areas of interest include, but are not limited to:
- Distributed and Cooperative Control
- Fault Detection and Robust Control
- Formation and Containment Control
- Fuzzy and Neural Network Control
- Synchronization and Optimization
- Emerging Control Paradigms
- Data Processing and Distributed Computing
- Social Network and Behavioral Simulations
- Cybersecurity and Privacy
- Multiattribute Group Decision Making
- Knowledge Representation and Sharing
- Autonomous Systems and Robotics
- Smart Cities and Urban Management
- Healthcare and Medicine
- Innovation in AI and Machine Learning
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