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The development of informational technology gives a lot of new possibilities to use computer in other non standard fields of using. I have in mention a lot of researches where computer simulates human thinking, creating new solutions and resolving different problems in a way of intelligence data processing. Artificial creativity is a growing area of artificial intelligence, which draws from and adds to diverse areas such as visual art, music, mathematics, science, literature and humour, as well as traditional areas such as cognitive science and computer science.

There are many definitions of human creativity but one of them include the ability to take existing objects and combine them in different ways for new proposes, wit other words the creativity is the action of combining previously not combined structures or objects. Creativity is the ability to create novel and useful ideas and solutions to everyday problems and challenges. Creativity is also the origination of new ideas that were previously non-existent. Often great solutions are products of silly ideas. Silly ideas are those that do not seem to follow rules, and are consequently thrown out, but modification of these ideas can result in a different path to the desired objective through unusual connections. 

And now a short analysis of what, in the most general terms, artificial creativity (AC) is. AC is a field that tries to determine in computational details how creativity works. But for AC to play this role we need to be able to understand creativity of different non-human kinds and believe that this is something that human-designed machines can achieve. If we can now construct successful models that accord with general account of creativity, then we can and should think of them as creative systems. Now is opening a very important question: how to make a computer creative if we know that computers can only blindly follow fixed rules defined in the program. We find the answer of this question in the special types of programmes called genetic algorithm. The genetic algorithm is a model of machine learning, which derives its behaviour from a metaphor of processes of evolution in nature. This is done by the creation within a machine of population of individual programs and then they go through a process of evolution using reproduction, selection, mutation and crossover operations producing new solutions.

Let see other explanation of AC from the point of view of a computer program. Creativity of a computer program is how good it is at finding unexpected solutions to problems. There are two very important key words here: solutions and unexpected. It is important that the creative solutions actually solve something otherwise the most creative entity in the universe would be a random number generator. Unexpected solutions are fundamental for any kind of creativity. If the author of the program, or another person who understands how the program works can be surprised on generated solutions it means that they find it creative. In a program that isnít creative, the programmer can understand exactly how the program arrived to a particular solution. A creative program arrives to solutions, that are both unexpected by people and the author canít understand the exact of way of how the program managed to obtain the solution. Although the programmer understands the principle of how the program works, he canít mentally retrace the route taken by the program to arrive to a particular solution.

Different experts from the area of computer programming donít consider unpredictability as the possible solution of the program. Unpredictable solution could be caused only by the program or data error. All programs are essentially algorithmic and a programmer can in principle trace how a program has created something which can demystify the process and may detract form our judgement of its creativity. My answer is very simple: let them try to do.






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