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Of all publications in the section: 39
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Article
Осипов Г. С., Назаренко Г., Клейменова Е. и др. Искусственный интеллект и принятие решений. 2012. № 4. С. 51-60.
Added: Nov 15, 2013
Article
Михеенкова М. А., Финн В. К. Искусственный интеллект и принятие решений. 2010. № 3. С. 20-32.
Added: Mar 15, 2013
Article
Михеенкова М. А. Искусственный интеллект и принятие решений. 2010. № 1. С. 20-32.
Added: Mar 15, 2013
Article
Корнилина Е., Михайлов А., Петров А.П. Искусственный интеллект и принятие решений. 2014. № 4. С. 68-72.

The paper presents a new method for determining the proximity of political positions contained in manifestos, i.e. the election programs of political parties, as well as other documents published by the parties to attract voters, which is based on latent semantic analysis. The approach is based on the conjecture that the proximity of political positions reveals itself as syntagmatic proximity of texts of the manifestos. A detailed description of the algorithm is presented, which includes the preprocessing of text, breaking it into fragments, "fragment-word" matrix construction, its normalization, the use of singular value decomposition, and construction of proximity diagrams. Some conclusions obtained from this analysis are briefly outlined

Added: Oct 13, 2015
Article
Панов А. И., Осипов Г. С. Искусственный интеллект и принятие решений. 2017. № 4. С. 5-22.

According to modern theories of mental function's emergence and the role of neurophysio- logical processes therein, the mental function formation is associated with the existence or communica- tive synthesis of specific information structures containing three information types of different origin: in- formation coming from the external environment, information extracted from the memory and information coming from motivation centres. The binding of these components into a single entity is en- sured by naming them; this also provides for the emerging structures’ stability. We call such information structures as signs due to their resemblance to similar structures that have been studied in semiotics. The set of signs formed by the actor during activities and communication forms his sign based world model reflecting his ideas about the environment, himself and other actors. The sign based world model enables the setting and resolution of a number of tasks arising for intelligent agents and their coalitions during be- haviour modeling , such as goal-setting, purposeful behaviour synthesis, role distribution, and the interac- tion of agents in the coalition. The paper considers a special object - the causal matrix, which describes the structure of the sign components. Operations and relationships in the sign based world model simulat- ing of the psychological characteristics of human behaviour are determined on this basis.

Added: Jan 11, 2018
Article
Дмитриев М.Г., Ломазов В. Искусственный интеллект и принятие решений. 2014. № 1. С. 52-56.
Added: Nov 27, 2013
Article
Подиновский В. В., Нелюбин А. П. Искусственный интеллект и принятие решений. 2014. № 4. С. 83-95.
Added: Dec 18, 2014
Article
Ногин В. Д. Искусственный интеллект и принятие решений. 2008. № 1. С. 98-112.
Added: Dec 5, 2011
Article
Кравченко Т. К. Искусственный интеллект и принятие решений. 2013. № 4. С. 72-80.

In the article the results of the Expert Decision Support System (EDSS) development are presented. It is argued that analytical justification of alternatives may be executed relying on different decision making methods in the conditions of risk and uncertainty. Transferring of EDSS to a new technological platform, as well as addition of the interface in English allows to increase the number of users that can work with the system simultaneously. 

Added: Jan 23, 2014
Article
Соловьев Ф. Н., Чеповский А. М. Искусственный интеллект и принятие решений. 2017. № 1. С. 21-26.
Added: Mar 30, 2017
Article
Хорошевский В. Ф. Искусственный интеллект и принятие решений. 2013. № 2. С. 3-13.

Structured approach to processing of data patterns images and semantic interpretation of these patterns are discussed in the paper. Brief state of art overview in domain of a structured approach to pattern recognition and scene analysis is presented. Description of the proposed structural approach is conducted in the context of data patterns images analysis and semantic interpretation data patterns received with the usage of classical methods of statistical analysis of indicators of science, education and innovation in the regions of Russia.

Added: Aug 9, 2013
Article
Кузьминов И. Ф., Бахтин П. Д., Тимофеев А. А. и др. Искусственный интеллект и принятие решений. 2020. № 1. С. 3-16.

The article is devoted to a review of the latest natural language processing (NLP) technologies that can be applied in strategic analytics. The introduction discusses the main problems in this area and specific tasks that can be solved using NLP tools. The article provides an overview of the main application areas in which these tools are involved. The paper reviews recent advancements in NLP and assess their potential. Conclusions are drawn about how the NLP apparatus should be developed in order to fulfill the needs of strategic analytics in the future.

Added: May 6, 2020
Article
Кравченко Т. К., Середенко Н. Н. Искусственный интеллект и принятие решений. 2012. № 1. С. 39-46.
Added: Jul 27, 2012
Article
Савченко А. В. Искусственный интеллект и принятие решений. 2013. № 4. С. 45-56.

Statistical pattern recognition was reduced to the hypothesis test for homogeneity. The probabilistic neural network (PNN) modification was proposed to achieve its optimal decision in terms of minimum Bayes-risk. The comparative analysis' results of the proposed modification with an original PNN were presented in a problem of automatic author identification

Added: Dec 23, 2013
Article
Ногин В. Д. Искусственный интеллект и принятие решений. 2010. № 2. С. 54-63.
Added: Dec 5, 2011
Article
Подиновский В. В. Искусственный интеллект и принятие решений. 2008. № 4. С. 3-11.
Added: Apr 22, 2010
Article
Панов А. И. Искусственный интеллект и принятие решений. 2018. № 2. С. 21-35.
Added: Sep 3, 2018
Article
Чепыжов В. В., Бедринцев А., Чернова С. Искусственный интеллект и принятие решений. 2015. № 2. С. 35-44.

This paper proposes an approach to obtaining of the set of admissible values of the optimization variables (design space) in the form of extreme ellipsoids describing a given set of points and inscribed in a given set of linear constraints. Considered ellipsoids include Principal Component’s ellipsoid, minimal volume ellipsoid and ellipsoid with minimal trace of its matrix containing given points. We have developed the procedures which change ellipsoid built based on points set exclusively in order to inscribe it into polyhedron. Ellipsoids are constructed by solving corresponding optimization problems which are formulated as convex programming problems using linear matrix inequalities.

Added: Mar 25, 2016
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