Multi-agent systems (MAS) often deal with complex applications that require distributed problem solving. In many applications, the individual and collective behavior of the agents depends on the observed data from distributed sources. This book discusses a number of research issues concerned with the use of Multi-Agent Systems for Data Mining (MADM), also known as agent-driven data mining. In addition, optimization algorithms are very important in modern research and industrial areas. This book examines one multi-population co-genetic algorithm (MPAGA) with double chain-like agent structure to realize parallel optimization, combining chain-like agent structure and multi-population parallel searching. Furthermore, this book proposes an efficient modular artificial neural network (ANN) architecture for the intelligent decision making of a robot in a robot soccer systems with different team configurations. Other chapters review the use of radio frequency identification (RFID) technology with supply chain agents and then analyze the security requirements, describe how to design and implement a large-scale multi-agent simulation software, and provide a framework of evacuation simulation for urban hazards such as flooding with effective agent's interaction tools with other agents and the physical environment.