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This article is cited in 3 scientific papers (total in 3 papers)
System analysis, management and information processing
Autonomous formation of a user model based
on digital footprint data in the internet space based
on training multi-agent neurocognitive architectures
Z. V. Nagoeva, K. Ch. Bzhikhatlova, O. V. Nagoevab, Z. A. Sundukovb, S. A. Kankulovb a FSBSE «Federal scientific center «Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences», 360002, KBR, Nalchik, 2, Balkarova street
b Institute of Computer Science and Problems of Regional Management – Branch of Federal public budgetary scientific establishment «Federal scientific center «Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences»,
360000, KBR, Nalchik, 37-a, I. Armand St.
Abstract:
The basic principles of simulation of user behavior in the Internet space using multi-agent neurocognitive architectures have been developed. Basic scenarios and algorithms for the use of an intelligent agent control system based on a multi-agent neurocognitive architecture for extracting data necessary for the creation and development of personalized simulation models have been constructed.
The results of this study can be used to create simulation models of the behavior of network systems users in the tasks of supporting systems of "enveloping intelligence", intelligent content analysis, predictive analytics based on distributed multi-agent modeling.
Keywords:
artificial intelligence, behavior modeling, cognitive architectures, multi-agent systems, enveloping intelligence systems, predictive analytics, digital footprint.
Received: 09.12.2020
Citation:
Z. V. Nagoev, K. Ch. Bzhikhatlov, O. V. Nagoeva, Z. A. Sundukov, S. A. Kankulov, “Autonomous formation of a user model based
on digital footprint data in the internet space based
on training multi-agent neurocognitive architectures”, News of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences, 2020, no. 6, 52–67
Linking options:
https://www.mathnet.ru/eng/izkab251 https://www.mathnet.ru/eng/izkab/y2020/i6/p52
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Abstract page: | 56 | Full-text PDF : | 46 | References: | 8 |
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