The integration of information is a central issue for Artificial Intelligence research and development. The inference process in AI is the fundamental mechanism for combining information, and a significant aspect of most AI systems is the means by which they manage their overall workload by focusing processing attention and controlling which inferences are drawn and when it is appropriate to draw them. Several perspectives on the control of inferential processes and their access to information have evolved. One view of the problem treats the task as a goal-driven perceptual process, where specific information is explicitly sought from the world through selected sensor modalities, translated into a common “vocabulary,” fused with other relevant information, and finally translated back into an understanding of critical aspects of the environment. Another view, centers on a flexible structure known as the blackboard architecture for enforcing control and communication activities. In this paper, we first review briefly a variety of AI inference techniques, focusing primarily on logical inference and uncertain reasoning methods. We conclude with a survey of approaches used to control inference processes, to mediate their access to real world information, and to schedule their activities.