Research and Development in Intelligent Systems XXXIII

Abstract

s Anytime algorithms give intelligent systems the capability to trade deliberation time for quality of results. This capability is essential for successful operation in domains such as signal interpreta-tion, real-time diagnosis and repair, and mobile robot control. What characterizes these domains is that it is not feasible (computationally) or de-sirable (economically) to compute the optimal answer. This article surveys the main control problems that arise when a system is composed of several anytime algorithms. These problems re-late to optimal management of uncertainty and precision. After a brief introduction to anytime computation, I outline a wide range of existing solutions to the metalevel control problem and describe current work that is aimed at increasing the applicability of anytime computation. A nytime algorithms 1 are algorithms whose quality of results improves gradually as computation time increases. The term anytime algorithm was coined by Dean and Boddy in the mid-1980s in the context of their work on time-dependent planning (Dean and Boddy 1988; Dean 1987). Dean and Boddy introduced several deliberation scheduling techniques that make use of per-formance profiles (PPs) to make time-alloca-tion decisions. A similar technique, termed flexible computation, was introduced by Horvitz (1990, 1987) to solve time-critical de-cision problems. This line of work is also closely related to the notion of limited ratio-nality in automated reasoning and search (Russell and Wefald 1991, 1989; Doyle 1990; D'Ambrosio 1989). Within the systems com-munity, a similar idea termed imprecise com-putation was developed by Jane Liu and others (1991). What is common to these research ef-forts is the recognition that the computation time needed to compute precise or optimal solutions will typically reduce the overall util-ity of the system. In addition, the appropriate level of deliberation can be situation depen-dent. Therefore, it is beneficial to build sys-tems that can trade the quality of results against the cost of computation. A rapid growth in the development of any-time algorithms in recent years has led to a number of successful applications in such ar-eas as the evaluation of Bayesian networks (Wellman and Liu 1994; Horvitz, Suermondt, and Cooper 1989), model-based diagnosis (Pos 1993), relational database query process-ing (Vrbsky, Liu, and Smith 1990), constraint-satisfaction problems (Wallace and Freuder 1995), and sensor interpretation and path planning (Zilberstein 1996; Zilberstein and Russell 1993). This article describes the con-struction, composition, and control of such algorithms.