This paper is concerned with the general problem of designing a system in which an agent with limited computational resources is required to respond in a timely manner to situations arising in a dynamic and uncertain environment. Special cases include designing the controller for a robot planetary explorer or designing the avionics system for a commercial aircraft. Existing approaches to solving such design problems differ in their characterization of the computational resources available to the system at run time and the knowledge of the environment available to the designer at design time. Even in cases in which these characterizations are precise, the resulting design problems often defy detailed analysis. We provide a general framework for characterizing these design problems that admits a careful analysis and provides insight into the tradeoffs inherent in resource-hounded decision making.
Recent years have seen a resurgence of interest in the use of metacognition in intelligent systems. This article 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 article 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).
Performance profile trees have recently been proposed as a theoretical basis for fully normative deliberation control. In this paper we conduct the first experimental study of their feasibility and accuracy in making stopping decisions for anytime algorithms on optimization problems. Using data and algorithms from two different real-world domains, we compare performance profile trees to other well-established deliberation-control techniques. We show that performance profile trees are feasible in practice and lead to significantly better deliberation control decisions. We then conduct experiments using performance profile trees where deliberationcontrol decisions are made using conditioning on multiple features of the solution to illustrate that such an approach is feasible in practice.