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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

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Last updated on Tuesday August 12, 2008 at 20:03:00 PDT (GMT -0700)

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