The science of complex systems provides the conceptual and computational tools to model dynamic systems in physical, biological, social and engineering fields. Understanding coordination mechanism of complex system is a challenging task insofar as complex systems are characterized by a large number of interacting components, chaotic behaviors, non-linearities, etc. On one hand, machine learning has permeated virtually all human activities in which data-analytics is required. Although complex system modeling lies in some sense opposite to non-interpretable machine learning, it has been verified that interpretable cooperative learning can contribute largely to understanding physical principles of complex systems. This includes inferring structural relationships of complex systems, cooperative estimating model variables and (hyper) parameters, identifying and predicting dynamics of system evolution, and other related aspects. On the other hand, cooperative control and optimization techniques have been successfully applied in most typical complex systems, since they combine intelligent information processing, interacting information feedback, and cooperative control/decision-making in solving complex tasks that are difficult to be solved without cooperation of interacting subsystems.
This Research Topic will focus on new analysis and synthesis approaches for cooperative learning, control and optimization in complex systems, and other related potential fields. Various aspects of the algorithms/strategies/techniques with cooperative learning, control, and optimization applied to complex systems, and applying complex system techniques to specific social and engineering systems are welcome. It aims to provide a platform for researchers in various fields such as applied mathematics, social science, control engineering, as well as computer science to present, share and summarize the most recent developments and ideas on related topics.
We welcome submissions covering, but not limited to, the following:
• Learning cooperation mechanisms of complex systems
• Cooperative learning method design for complex systems
• Structural causality discovery for complex systems
• Cooperative optimization and control for complex systems
• Collective behavior analysis and modeling
• Multiple unmanned systems cooperation and competitive game
The science of complex systems provides the conceptual and computational tools to model dynamic systems in physical, biological, social and engineering fields. Understanding coordination mechanism of complex system is a challenging task insofar as complex systems are characterized by a large number of interacting components, chaotic behaviors, non-linearities, etc. On one hand, machine learning has permeated virtually all human activities in which data-analytics is required. Although complex system modeling lies in some sense opposite to non-interpretable machine learning, it has been verified that interpretable cooperative learning can contribute largely to understanding physical principles of complex systems. This includes inferring structural relationships of complex systems, cooperative estimating model variables and (hyper) parameters, identifying and predicting dynamics of system evolution, and other related aspects. On the other hand, cooperative control and optimization techniques have been successfully applied in most typical complex systems, since they combine intelligent information processing, interacting information feedback, and cooperative control/decision-making in solving complex tasks that are difficult to be solved without cooperation of interacting subsystems.
This Research Topic will focus on new analysis and synthesis approaches for cooperative learning, control and optimization in complex systems, and other related potential fields. Various aspects of the algorithms/strategies/techniques with cooperative learning, control, and optimization applied to complex systems, and applying complex system techniques to specific social and engineering systems are welcome. It aims to provide a platform for researchers in various fields such as applied mathematics, social science, control engineering, as well as computer science to present, share and summarize the most recent developments and ideas on related topics.
We welcome submissions covering, but not limited to, the following:
• Learning cooperation mechanisms of complex systems
• Cooperative learning method design for complex systems
• Structural causality discovery for complex systems
• Cooperative optimization and control for complex systems
• Collective behavior analysis and modeling
• Multiple unmanned systems cooperation and competitive game
