Eric A. Hansen, Shlomo Zilberstein
Anytime algorithms offer a tradeoff between solution quality and computation time that has proved useful in applying artificial intelligence techniques to time-critical problems. To exploit this tradeoff, a system must be able to determine the best time to stop deliberation and act on the currently available solution. When the rate of improvement of solution quality is uncertain, monitoring the progress of the algorithm can improve the utility of the system. This paper introduces a technique for run-time monitoring of anytime algorithms that is sensitive to the variance of the algorithm’s performance, the time-dependent utility of a solution, the ability of the run-time monitor to estimate the quality of the currently available solution, and the cost of monitoring. The paper examines the conditions under which the technique is optimal and demonstrates its applicability.