Control Systems and Computers, N2, 2017, Article 1


Upr. sist. maš., 2017, Issue 2 (268), pp. 6-19, 37.

UDC 519.816

Tymofejeva Nadeshda К., Doctor of Engineering Sciences, Senior Researcher, E-mail:,

Grytsenko Vladimir I., Corresponding Member of the NAS of Ukraine,

International Research and Training Centre of Information Technologies and Systems of the NAS and MES of Ukraine, Glushkov ave., 40, Kyiv, 03187, Ukraine,

Combinatorial is in a problems of artificial intellect

Introduction. The problems of the artificial intelligence are difficult by nature and are not always amenable to formalization. A lot of the applied problems of this class are reduced to the problems of the combinatorial optimization. This is because their vast part requires sorting of variants. A combinatorial nature is the characteristic of the search problems. The design methods do not always explain the search nature of the artificial intelligence problems. The detailed analysis of the problems of this class shows that the argument for the objective function is the different types of the combinatorial configurations.

Method. The method of creating the artificial intelligence with the use of the combinatorial optimization theory is represented. An objective function and defined argument for the combinatorial configurations of different types are formulated. As the system analysis shows, in the problems of this class the combinatorial configurations can be the argument for the objective function and input data. The use of the combinatorial optimization theory allows to set the combinatorial nature, to formulate the objective function, to identify the characteristics signs, which establish the similarity of the artificial intelligence problems.

Results. The expounded researches allow the identification the uncertainty cause of different kinds, which arises up in the process of their decision, and explain the nature of the input data vagueness.

Keywords: artificial intellect, uncertainty, combinatorial configuration, combinatorial optimization objective function, similarity of problems of combinatorial optimization.

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Received 11.01.2017