The emerging field of resource-bounded reasoning is concerned with the development, composition, and meta-level control of flexible computational methods that allow small quantities of resources, such as time, memory, or information, to be traded for gains in the value of computed results. A better understanding of these techniques is essential for the construction of useful intelligent systems for automated planning, diagnosis, decision making, and control. There are two primary components to this research project. One is the development of resource-bounded techniques for automated information gathering from a network of heterogeneous, dynamic information sources. The approach extends the classical theory of information value by formalizing the effect of such factors as information reliability, retrieval costs, real-time operation, and uncertainty. A second and closely related activity improves current planning technology by constructing planning systems that can operate in real-time under uncertainty and under variable computational constraints.
