Controlling evolutionary network dynamics

Advances in control technology and communication technology have enabled the evolution of multi-agent networks into large-scale systems that could complete cooperative tasks which are more complicated than ever before. This research project aims at proposing new distributed control methodologies and algorithms to enable complex decision-making in multi-agent systems to execute complex cooperative tasks rapidly,
accurately and robustly. Simple local information passing, storage and processing are critical to accomplish this goal for large-scale multi-agent networks. Using the tool of evolutionary game theory, smart
information exchange architectures within multi agent systems are  proposed, which are more likely to lead to the emergence of novel cooperative control strategies that could cope with difficult situations.