Recent years have seen a resurgence of interest in the use of metacognition in intelligent systems. This essay is part of a small section meant to give interested researchers an overview and sampling of the kinds of work currently being pursued in this broad area. The current essay offers a review of recent research in two main topic areas: the monitoring and control of reasoning (metareasoning) and the monitoring and control of learning (metalearning). What is metacognition in computation? Rosie (the robot maid from the TV show The Jetsons) spends her days cooking, cleaning, ironing, and attending to the usual household tasks of late 21 st century life. Because of a bug in one of her memory chips, however, she almost always forgets to buy dog food when she goes out. She has an adequate recovery plan for this: she simply feeds Astro some of the Jetson’s dinner. But 21 st century human food is expensive, so this strategy is wasteful. Realizing this, and recognizing that she has forgotten several times, Rosie adopts a special strategy to help her remember: she sticks the spare dog collar in her

Abstract. In the Meta-ORB reflective middleware architecture, runtime models provide the necessary meta-information to instantiate specialized platform configurations and to construct the reflective self-representation of base-level systems. Other kinds of useful meta-information may also be provided by the system’s runtime model, such as policies that allow the middleware to adapt itself automatically. Evolving the middleware meta-model and extending its infrastructure to handle new kinds of model-based constructs may be considerably complex and would require re-implementation of several parts of the middleware. In this paper we present an approach for the management of runtime models and metamodels, aiming to simplify the evolution of the middleware so that it can support new kinds of constructs defined in its meta-model. Key words: runtime models, meta-modeling, reflective middleware 1