A blackboard architecture for control

The control problem—which of its potential actions should an AI system perform at each point in the problem-solving process?—is fundamental to all cognitive processes. This paper proposes eight behavioral goals for intelligent control and a ‘blackboard control architecture’ to achieve them. It enables AI systems to operate upon their own knowledge and behavior and to adapt to unanticipated problem-solving situations. The paper shows how opm, a blackboard control system for multiple-task planning, exploits these capabilities. It also shows how the architecture would replicate the control behavior of hearsay-ii and hasp. The paper contrasts the blackboard control architecture with three alternatives and shows how it continues an evolutionary progression of control architectures.