Anytime algorithm: Difference between revisions

Revision as of 21:35, 27 January 2025

Revision as of 10:11, 1 February 2025

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== Goals ==

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">{{harvnb|Zilberstein|1996}} </ref> They are also supposed to be flexible in time and resources.<ref name="Grass">{{cite journal |first=J. |last=Grass |title=Reasoning about computational resource allocation. XRDS: Crossroads |journal=The ACM Magazine for Students |volume=3 |issue=1 |pages=16–20 |date=1996 |doi=10.1145/332148.332154 |s2cid=45448244 |doi-access=free }}</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=https://web.archive.org/web/20131213011435/http://ai.eecs.umich.edu/cogarch2/cap/anytime.plan|archivedate=13 December 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 December 2013}}</ref>

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There are many famous anytime algorithms in the literature. We list here some of them.

* PBVI (point-based value iteration) is an anytime algorithm for [[Automated planning and scheduling|automated planning]] in [[Partiallypartially observable Markov decision process|partially observable Markov decision processes]]es.<ref>{{Cite journal |last1=Pineau |first1=Joelle |last2=Gordon |first2=Geoff |last3=Thrun |first3=Sebastian |date=2003-08-09 |title=Point-based value iteration: an anytime algorithm for POMDPs |url=https://dl.acm.org/doi/10.5555/1630659.1630806 |journal=Proceedings of the 18th International Joint Conference on Artificial Intelligence |series=IJCAI'03 |___location=San Francisco, CA, USA |publisher=Morgan Kaufmann Publishers Inc. |pages=1025–1030 }}</ref>.

==References==