Monitoring anytime algorithms

Abstract

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. If there is uncertainty about how much solution quality will improve with computation time, or about how the problem state may change after the start of the algorithm, monitoring the algorithm's progress and/or the problem state can make possible a better stopping decision and so improve the utility of the system. This paper analyzes the issues involved in run-time monitoring of anytime algorithms. It reviews previous work and casts the problem in a new framework from which some improved monitoring strategies emerge.