Anytime algorithm: Difference between revisions

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The goal of anytime algorithms are to give [[Hybrid intelligent system|intelligent systems]] the ability to make results of better quality in return for turn-around time.<ref name="Zilberstein">Zilberstein, Shlomo. "Using Anytime Algorithms in Intelligent Systems". http://rbr.cs.umass.edu/shlomo/papers/Zaimag96.pdf</ref> They are also supposed to be flexible in time and resources.<ref name="Grass">Grass, Joshua. "Reasoning about [[Computational resource|Computational Resource]] Allocation." http://www.acm.org/crossroads/xrds3-1/racra.html</ref> They are important because [[artificial intelligence]] or AI algorithms can take a long time to complete results. This algorithm is designed to complete in a shorter amount of time.<ref name="Grass"/> Also, these are intended to have a better understanding that the system is dependent and restricted to its agents and how they work cooperatively.<ref name="Grass"/> An example is the [[Newton-Raphson]] iteration applied to finding the square root of a number.<ref name="FOLDOC">[http://foldoc.org/anytime+algorithm anytime algorithm from Free Online Dictionary of Computing (FOLDOC)]</ref> Another example that uses anytime algorithms is trajectory problems when you're aiming for a target; the object is moving through space while waiting for the algorithm to finish and even an approximate answer can significantly improve its accuracy if given early.<ref name="Grass"/>

What makes anytime algorithms unique is their ability to return many possible outcomes for any given input.<ref name="Zilberstein"/> An anytime algorithm uses many well defined quality measures to monitor progress in [[problem solving]] and [[distributed computing]] resources.<ref name="Zilberstein"/> It keeps searching for the best possible answer with the amount of time that it is given.<ref name="umich">{{cite web|title=Anytime algorithms|url=http://ai.eecs.umich.edu/cogarch2/index.html|website=Cognitive architectures|publisher=University of Michigan Artificial Intelligence Laboratory|archiveurl=httphttps://web.archive.org/web/20131213011435/http://ai.eecs.umich.edu/cogarch2/cap/anytime.plan|archivedate=13 Dec 2013}}</ref> It may not run until completion and may improve the answer if it is allowed to run longer.<ref name="elook">{{cite web|title=Anytime algorithm - Computing Reference|url=http://www.elook.org/computing/anytime-algorithm.htm|website=eLook.org|archiveurl=https://web.archive.org/web/20131212094200/http://www.elook.org/computing/anytime-algorithm.htm|archivedate=12 Dec 2013}}</ref>

This is often used for large decision set problems.<ref name="Horsch"/> This would generally not provide useful information unless it is allowed to finish.<ref name="Bender">Bender, Edward A. ''Mathematical Methods In Artificial Intelligence'', [[IEEE Computer Society]] Pres, 1996</ref> While this may sound similar to [[dynamic programming]], the difference is that it is fine-tuned through random adjustments, rather than sequential.