In this paper we outline a general approach to the study of metareasoning, not in the sense of explicating the semantics of explicitly specified meta-level control policies, but in the sense of providing a basis for selecting and justifying computational actions. This research contributes to a developing attack on the problem of resource-bounded rationality, by providing a means for analysing and generating optimal computational strategies. Because reasoning about a computation without doing it necessarily involves uncertainty as to its outcome, probability and decision theory will be our main tools. We develop a general formula for the utility of computations, this utility being derived directly from the ability of computations to affect an agent's external actions. We address some philosophical difficulties that arise in specifying this formula, given our assumption of limited rationality. We also describe a methodology for applying the theory to particular problem-solving systems, a...

Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decision-theoretic framework. Recent analyses of the restrictions of several traditional AI reasoning techniques, coupled with the development of more tractable and expressive decisiontheoretic representation and inference strategies, have stimulated renewed interest in decision theory and decision analysis. We describe early experience with simple probabilistic schemes for automated reasoning, review the dominant expert-system paradigm, and survey some recent research at the crossroads of AI and decision science. In particular, we present the belief network and influence diagram representations. Finally, we discuss issues that have not been studied in detail within the expert-systems sett...

This work is an adaptation of Heckerman (1991). All figures and tables are printed with permission from MIT Press. We present an evaluation of the diagnostic accuracy of Pathfinder, an expert system that assists pathologists with the diagnosis of lymph-node diseases. We evaluate two versions of the system using both informal and decision-theoretic metrics of performance. In one version of Pathfinder, we assume incorrectly that all observations are conditionally independent. In the other version, we use a belief network to represent accurately the probabilistic dependencies among the observations. In both versions, we make the assumption—reasonable for this domain—that diseases are mutually exclusive and exhaustive. The results of the study show that (1) it is cost effective to represent probabilistic dependencies among observations in the lymph-node domain, and (2) the diagnostic accuracy of the more complex version of Pathfinder is at least as good as that of the Pathfinder expert. In addition, the study illustrates how informal and decision-theoretic metrics for performance complement one another. 2 1

We consider the problem of finding solutions to resource-based configuration problems under real-time conditions. The objective of finding the best solutions possible, given the time restrictions, is formalized within a decision theoretic framework. The utility of a configuration returned by a search algorithm depends on the cost of this configuration as well as on the time required to find it. We propose a heuristic search strategy as an approximation of a (computationally intractable) search algorithm which is optimal in the sense that it maximizes the (expected) value of computation. This search strategy consists of two phases. In connection with the heuristic principles underlying both search phases, the decision whether to continue or break off the search along a certain path of the search tree has to be made. We propose a formalization of as well as a solution to this termination problem. Experimental studies show very promising results for the search algorithm. 1 Introduction H...