Author Description

Due to rapid developments in computing, communication, and sensing technology, multi-agent systems have become increasingly ubiquitous. Their applications include mobile sensor networks, autonomous vehicles, intelligent transportation systems, and smart grids. The complex unknown environment and inaccurate dynamics prose additional challenges for the modeling, control, and optimization of such systems. Therefore, data science and machine learning are providing opportunities to develop artificial intelligence-based methods and enable new control and optimization paradigms for multi-agent systems. The aim of this Special Issue is to bring together significant developments in the interface between machine learning, neurodynamics, and swarm intelligence.