Monitoring and control of anytime algorithms: A dynamic programming approach

Monitoring and control of anytime algorithms: A dynamic programming approach

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Article ID: iaor20023727
Country: United States
Volume: 126
Issue: 1/2
Start Page Number: 139
End Page Number: 157
Publication Date: Feb 2001
Journal: Artificial Intelligence
Authors: ,
Keywords: heuristics
Abstract:

Anytime algorithms offer a tradeoff between solution quality and computation time that has proved useful in solving time-critical problems such as planning and scheduling, belief network evaluation, and information gathering. To exploit this tradeoff, a system must be able to decide when to stop deliberation and act on the currently available solution. This paper analyzes the characteristics of existing techniques for meta-level control of anytime algorithms and develops a new framework for monitoring and control. The new framework handles effectively the uncertainty associated with the algorithm's performance profile, the uncertainty associated with the domain of operation, and the cost of monitoring progress. The result is an efficient non-myopic solution to the meta-level control problem for anytime algorithms.

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