Multiagent Robotic Systems addresses learning and adaptation in decentralized autonomous robots. It provides a guided tour of the pioneering work and major technical issues in multiagent robotics research. Its systematic examination demonstrates the interrelationships between the autonomy of individual robots and the emerged global behavior properties of a group performing a cooperative task. The author also includes descriptions of the essential building blocks of the architecture of autonomous mobile robots with respect to their requirement on local behavioral conditioning and group behavioral evolution.

MOTIVATION, APPROACHES, AND OUTSTANDING ISSUES. Why Multiple Robots? Towards Cooperative Control. Approaches. Models and Techniques. Outstanding Issues. CASE STUDIES IN LEARNING. Multiagent Reinforcement Learning: Techniques. Multiagent Reinforcement Learning Results. Multiagent Reinforcement Learning: What Matters. Evolutionary Multiagent Reinforcement Learning. CASE STUDIES IN ADAPTATION. Coordinated Maneuvers in a Dual-Agent System. Collective Behavior. CASE STUDIES IN SELF-ORGANIZATION. Multiagent Self-Organization. Evolutionary Multiagent Self-Organization. AN EXPLORATION TOOL. Toolboxes for Multiagent Robotics. INDEX.