Guest Editor(s)
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- Prof. Xin Xu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China.
Website | E-mail
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- Prof. Zongzhang Zhang
- School of Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, China.
Website | E-mail
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- Prof. Jianye Hao
- College of Intelligence and Computing, Tianjin University, Tianjin, China.
Website | E-mail
Special Issue Introduction
Nowadays, artificial intelligence is moving from individual intelligence to collective intelligence. As the core research field of artificial intelligence, the development of multi-agent systems is a major thrust towards general artificial intelligence. The research on multi-agent learning is an interdisciplinary subject in different fields, such as cybernetics, machine learning, operations research, and game theory. A multi-agent system is composed of multiple agents, which interact in a shared environment to achieve common or conflicting goals and has a wide range of application prospects in the field of unmanned systems, such as unmanned vehicles, unmanned aircraft, unmanned ships, unmanned submarines, etc.
However, learning in multi-agent environments has to deal with challenging problems such as high-dimensionality, distributed incomplete information, complex cooperation mechanism, convergence to optimal solutions with nonstationary environments. As an emerging field, the multi-agent system based on learning has many research prospects in the theoretical basis of multi-agent systems, distributed optimization methods, and other theoretical methods, as well as the basic platform of multi-agent systems and innovative applications. The focus of this special issue is to bring together the research progress and ideas from different aspects in the field of multi-agent learning so that the research in this area can be effectively promoted.
Scope of the Special Issue
We invite submissions on all topics of research on the multi-agent system based on learning, including but not limited to:
● Multi-agent system decision-making and planning
● Learning methods for multi-agent sensing and positioning technology
● Multi-agent reinforcement learning
● Learning-based multi-agent cooperative control
● Deep reinforcement learning for multi-agent systems
● Interaction mechanisms for learning in multi-agent systems
● Learning for multi-agent communication
● Innovative application of multi-agent systems
● Applications of multi-agent learning in real-world problems
Submission Deadline
31 Dec 2022
Intelligence & Robotics
