Control, Learning, and Games of Complex Systems: Theory and Methods

Chair:
Yuanhua Ni Nankai University, China

Key Words: Optimal Control, Learning, Game Theory, Dynamical Systems

Information: This special session focuses on control, learning, and game-theoretic frameworks for complex dynamical systems, addressing fundamental problems in modeling, analysis, and methodology under structural complexity, information constraints, and interacting decision-makers. As modern systems grow in scale and coupling, control theory is increasingly integrated with learning methods and game theory, leading to new analytical paradigms and solution approaches. The session welcomes theoretical contributions that explore unified perspectives of control, learning, and games, including stability and performance analysis, optimal and robust control, learning-based and adaptive decision-making, and coordination and competition in multi-agent systems. The goal is to advance the theoretical foundations of control and decision-making in complex systems.

Topics of interest include but are not limited to: 

• Optimal control theory and methods for dynamical systems
• Learning methods in control systems
• Game theory and decision-making in dynamical systems
• Control and decision-making in multi-agent systems
• Distributed and decentralized optimal control
• Intersections of control, learning, and games
• Interaction and decision-making in networked systems
• Optimization and decision-making in control systems

Submission Deadline: April 28, 2026 (the first round)