Anytime Algorithm - Goals

Goals

The goal of anytime algorithms are to give intelligent systems the ability to make results of better quality in return for turn-around time. They are also supposed to be flexible in time and resources. 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. Also, these are intended to have a better understanding that the system is dependent and restricted to its agents and how they work cooperatively. An example is the Newton-Raphson iteration applied to finding the square root of a number. 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.

What makes anytime algorithms unique is their ability to return many possible outcomes for any given input. 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 so that it can be told to stop at any time and would return the best result it has found so far. This is why it is called an interruptible algorithm. Anytime algorithms also maintain the last result, so that if they are given more time, they can continue from where they left off to obtain an even better result.

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