Anytime algorithm

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1 Introduction
2 Names
3 Goals
4 Construction
5 Decision Trees
6 Performance Profile
7 Algorithm Prerequisites
8 References
9 Recommended Reading

[edit] Introduction

Most algorithms run to completion: they provide a single answer after performing some fixed amount of computation. In some cases, however, the user may wish to terminate the algorithm prior to completion. The amount of the computation required may be substantial, for example, and computational resources might need to be reallocated. Most algorithms either run to completion or they provide no useful solution information. Anytime algorithms, however, are able to return a partial answer, whose quality depends on the amount of computation they were able to perform.

[edit] Names

An anytime algorithm may also called an "interruptible algorithm". They are different from contact algorithms, which must declare a time in advance; in an anytime algorithm, a process can just announce that it is terminating ^[1].

[edit] 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 ^[2]. They are also suppose to be flexible in time and resources ^[3]. 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 ^[4]. Also, these are intended to have a better understanding that the system is dependent and restricted to its agents and how they work cooperatively ^[5]. An example the is Newton-Raphson iteration applied to finding the square root of a number b ^[6]. Another example tat uses anytime algorithms is trajectory problems and your aiming for a target ^[7].

What makes anytime algorithms unique is their ability to return many possible outcomes for any given output ^[8]. It uses many well defined quality measures to monitor progress in problem solving and distributing computing resources ^[9]. It keeps searching for the best possible answer with the amount of time that it is given ^[10] It may not run until completion and may improve the answer if it is allowed to run longer ^[11]. This is often used for large decision set problems ^[12]. This would generally not provide useful information unless it is allowed to finish ^[13]. While this may sound similar to dynamic programming, the difference is that it is fine tuned trough random adjustments, rather than sequential.

Anytime algorithms are designed to be predictable ^[14]. Another goal is that someone can interrupt the process and the algorithm would give its most accurate result ^[15]. 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 ^[16].

[edit] Construction

Make an algorithm with a parameter that influences running time. For example, as time increases, this variable also increases. After for a period of time, the search is stopped without having the goal met. This is similar to Jeopardy when the time runs out ^[17]. The contestants have to represent what they believe is the closest answer, although they may not know it or come even close to figuring out what it could be. This is similar to an hour long test. Although the test questions are not in themselves limiting for time, the test must be completed within the hour. Likewise, the computer has to figure out how much time and resources to spend on each problem ^[18].

[edit] Decision Trees

When the decider has to act, there must be some ambiguity. Also, there must be some idea about how to solve this ambiguity. This idea must be translatable to a state to action diagram ^[19].

[edit] Performance Profile

The performance profile estimates the quality of the results based on the input and the amount of time that is allotted to the algorithm ^[20]. The better the estimate, the sooner the result would be found ^[21]. Some systems have a larger database that gives the probability that the output is the expected output ^[22]. It is important to note that one algorithm can have several performance profiles ^[23]. Most of the time performance profiles are constructed using mathematical statistics using representative cases. For example in the traveling salesman problem, the performance profile was generated using a user-defined special program to generate the necessary statistics ^[24]. In this example, the performance profile is the mapping of time to the expected results ^[25]. This quality can be measured in several ways:

certainty: where probability of correctness determines quality ^[26]

accuracy: where error bound determines quality ^[27]

specificity: where the amount of particulars determine quality ^[28]

[edit] Algorithm Prerequisites

Initial behavior: While some algorithms start with immediate guesses, others take a more calculated approach and have a start up period before making any guesses ^[29]

Growth direction: How the quality of the program changes with increasing runtime ^[30]

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 ^[31]