ALGORITHM
| Collection | International Encyclopedia of Systems and Cybernetics |
|---|---|
| Year | 2004 |
| Vol. (num.) | 2(1) |
| ID | ◀ 73 ▶ |
| Object type | Epistemology, ontology or semantics, Methodology or model |
A step-by-step ordered and finite set of prescribed operations along an optimal path, whose use permits the solution of a specific class of problems.
Any algorithm implies determinism.
J.Z. YOUNG gives a definition related to computation: “A program by which a complicated calculation is reduced to a long series of simple ones” (adding: “that a digital computer can perform”: see hereafter the comment on artificial — or natural - neural networks).(1978, p.289)
G. KLIR states: “The intuitive notion of an algorithm was formalized in several ways, including formalizations based on the concepts of TURING machines, MARKOV algorithms, and recursive functions, which were all proved to be equivalent” (1991, p.127)
An algorithm may be quite complex and contain a number of subordinated routines and instructions for their eventual use. However, the TURING machine is, at least in principle, a universal representation of all possible algorithms. The potential and global use of an algorithm is strictly limited to its content, which necessarily reflects an implicit and specific representation of its field of application.
The incompleteness concept(GÖDEL) is thus valid for any algorithm.
As to algorithms relations to natural or artificial intelligence, it can be argued that they are merely acquired properties of brains or computers. In the latter's case, they must be introduced in the machine as a product of natural intelligence. It is now however conceivable that artificial neural networks could become able to construct complex algorithms by learning.
According to R.W. FULLER and P.PUTNAM knowledge acquisition through teaching is so different from discovery and self-training, and sometimes quite stultifying.