As intelligent, autonomous systems are embedded in critical real-world environments, it becomes increasingly important to rigorously characterize how these systems will perform. Research in real-time computing and control has developed ways of proving that a given control system will meet the demands of an environment, but has not addressed the dynamic planning of control actions. Building an agent that can flexibly achieve its goals in changing environments requires a blending of real-time computing and AI technologies. The Cooperative Intelligent Real-time Control Architecture (CIRCA) implements this blending by executing complex AI methods and guaranteed real-time control plans on separate subsystems. We describe the formal model of agent/environment interactions that CIRCA uses to build control plans, and we show how those control plans are guaranteed to meet domain requirements. CIRCA's world model provides the information required to make real-time performance guarantees, but avoids unnecessary complexity.
