In recent years, a major thrust in addressing the requirements of adaptivity and responsiveness for manufacturing control has been the application of tools from distributed artificial intelligence. These tools can be called intelligent control systems. Typically, these tools are adaptable to a changing environment; resilient to disturbance; distributed, in the sense that typically more than one decision-making element exists; and dynamic in decision making. They range from modeling tools such as neural networks, fuzzy logic, and evolutionary programming to new distributed forms of manufacturing control and management systems. Of particular importance are multiagent-based manufacturing control and management systems. Such approaches bring new features of flexibility and easy reconfigurability to industrial-control solutions. These features result from agent-based systems' basic properties such as a high degree of autonomy for decision-making units, the ability to communicate complex messages asynchronously, the capability to negotiate and cooperate, and, mainly, the ability to achieve complex global goals without a central decision element.