Added to Favorites

Related Searches

Nearby Words

What makes anytime algorithms unique is their ability to return many possible outcomes for any given output. An anytime algorithm uses many well defined quality measures to monitor progress in problem solving and distributing computing resources. It keeps searching for the best possible answer with the amount of time that it is given. It may not run until completion and may improve the answer if it is allowed to run longer. This is often used for large decision set problems. This would generally not provide useful information unless it is allowed to finish. While this may sound similar to dynamic programming, the difference is that it is fine-tuned through random adjustments, rather than sequential.

Anytime algorithms are designed to be predictable. Another goal is that someone can interrupt the process and the algorithm would give its most accurate result. This is why it is called an interruptible algorithm. Another goal of anytime algorithms are to maintain the last result so as they are given more time, they can continue calculating a more accurate result.

- certainty: where probability of correctness determines quality
- accuracy: where error bound determines quality
- specificity: where the amount of particulars determine quality

- Growth direction: How the quality of the program's "output" or result, varies as a function of the amount of time ("run time")
- Growth rate: Amount of increase with each step. Does it change constantly, such as in a bubble sort or does it change unpredictably?
- End condition: The amount of runtime needed

- Anytime Algorithm http://tarono.wordpress.com/2007/03/20/anytime-algorithm
- http://www.acm.org/crossroads/xrds3-1/racra.html

- Boddy, M, Dean, T. 1989. Solving Time-Dependent Planning Problems. Technical Report: CS-89-03, Brown University
- Burgin, M. Multiple computations and Kolmogorov complexity for such processes, Notices of the Academy of Sciences of the USSR, 1983, v. 27, No. 2 , pp. 793-797
- Burgin M., Universal limit Turing machines, Notices of the Russian Academy of Sciences, 325, No. 4, (1992), 654-658
- Burgin, M. Super-recursive algorithms, Monographs in computer science, Springer, 2005
- Grass, J., and Zilberstein, S. 1996. Anytime Algorithm Development Tools. SIGART Bulletin (Special Issue on Anytime Algorithms and Deliberation Scheduling) 7(2)
- Michael C. Horsch and David Poole, An Anytime Algorithm for Decision Making under Uncertainty, In Proc. 14th Conference on Uncertainty in Artificial Intelligence (UAI-98), Madison, Wisconsin, USA, July 1998, pages 246-255.
- E.J. Horvitz. Reasoning about inference tradeoffs in a world of bounded resources. Technical Report KSL-86-55, Medical Computer Science Group, Section on Medical Informatics, Stanford University, Stanford, CA, March 1986
- Wallace, R., and Freuder, E. 1995. Anytime Algorithms for Constraint Satisfaction and SAT Problems. Paper presented at the IJCAI-95 Workshop on Anytime Algorithms and Deliberation Scheduling, 20 August, Montreal, Canada.
- Zilberstein, S. 1993. Operational Rationality through Compilation of Anytime Algorithms. Ph.D. diss., Computer Science Division, University of California at Berkeley.
- Shlomo Zilberstein, Using Anytime Algorithms in Intelligent Systems, AI Magazine, 17(3):73-83, 1996

Wikipedia, the free encyclopedia © 2001-2006 Wikipedia contributors (Disclaimer)

This article is licensed under the GNU Free Documentation License.

Last updated on Tuesday August 12, 2008 at 21:03:00 PDT (GMT -0700)

View this article at Wikipedia.org - Edit this article at Wikipedia.org - Donate to the Wikimedia Foundation

This article is licensed under the GNU Free Documentation License.

Last updated on Tuesday August 12, 2008 at 21:03:00 PDT (GMT -0700)

View this article at Wikipedia.org - Edit this article at Wikipedia.org - Donate to the Wikimedia Foundation

Copyright © 2014 Dictionary.com, LLC. All rights reserved.