Anytime Algorithms

Anytime algorithms are algorithms that trade execution time for quality of results. In particular, an anytime algorithm always has a best-so-far answer available, and the quality of the answer improves with execution time. The user may examine this answer at any time and choose to terminate, temporarily suspend, or continue the algorithm’s execution until completion. Using an anytime algorithm can mitigate the need for high performance hardware.

[1] Shlomo Zilberstein: Using Anytime Algorithms in Intelligent Systems. AI Magazine 17(3): 73-83 (1996)
[2] Xiaopeng Xi, Ken Ueno, Eamonn J. Keogh, Dah-Jye Lee: Converting non-parametric distance-based classification to anytime algorithms. Pattern Anal. Appl. 11(3-4): 321-336 (2008)