{"id":10321,"date":"2026-04-07T08:49:50","date_gmt":"2026-04-07T07:49:50","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=10321"},"modified":"2026-06-18T09:22:37","modified_gmt":"2026-06-18T08:22:37","slug":"25-6-19-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/25-6-19-en\/","title":{"rendered":"25.6.19 En"},"content":{"rendered":"\n

Neurocognitive learning algorithm for a multi-agent system for evolutionary modeling of gene expression according to PCR analysis of plants<\/strong><\/h1>\n\n\n\n

Z.V. Nagoev, M.I. Anchekov, Zh.Kh. Kurashev, A.A. Khamov<\/strong><\/p>\n\n\n\n

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Abstract<\/strong>.<\/strong> <\/em>The work is aimed at creating a methodology for using general artificial intelligence systems to manage the process of creating new plant hybrids with a given set of economically useful traits. The basic principles for creating plant simulation models based on multi-agent modeling based on enlarged conditional cell agents, the synthesis of whose behavior is carried out by the controling neurocognitive
architecture, have been developed. The basic principles for creating an automatic data collection system for evolutionary machine learning of intelligent expert systems for breeding and seed production based on robotic digital phenotyping and genetic data have been developed. An algorithm has been developed for training a decentralized system for controlling the growth and development of plant simulation models based on the identification of phenogenotypic characteristics of growth and development processes determined by the expression of plant genes.<\/p>\n\n\n\n

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Keywords<\/strong>:<\/em><\/strong> artificial general intelligence, multi-agent systems, neurocognitive architectures, plant breeding, gene expression, machine learning, digital phenotyping<\/p>\n\n\n\n

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For citation<\/strong>.<\/strong> Nagoev Z.V., Anchekov M.I., Kurashev Zh.Kh., Khamov A.A. Neurocognitive learning algorithm for a multi-agent system for evolutionary modeling of gene expression according to PCR analysis of plants. News of the Kabardino-Balkarian Scientific Center of RAS. 2023. No. 6(116). Pp. 179\u2013192. DOI: 10.35330\/1991-6639-2023-6-116-179-192<\/p>\n\n\n\n

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R<\/strong>eferences<\/summary>\n
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  1. Anchekov M.I., Bogotova Z.I., Pshenokova I.A. et al. Collaborative breeding system based on a consortium of heterogeneous intelligent agents. News of the Kabardino-Balkarian Scientific Center of RAS<\/em>. 2022. No. 5(109). Pp. 25\u201337. DOI: 10.35330\/1991-6639-2022-5-109-25-37. (In Russian)<\/li>\n\n\n\n
  2. Anchekov M.I., Bzhikhatlov K.Ch., Nagoev Z.V. et al. Ontoepisociophylogenetic development of general artificial intelligence systems based on multi-agent neurocognitive architectures. News of the Kabardino-Balkarian Scientific Center of RAS<\/em>. 2022. No. 6(110). Pp. 61\u201375. DOI: 10.35330\/1991-6639-2022-6-110-61-75. (In Russian)<\/li>\n\n\n\n
  3. Anchekov M.I., Apshev A.Z., Bzhikhatlov K.Ch. et al. Formal genome model of a general artificial intelligence agent based on multi-agent neurocognitive architectures. News of the Kabardino-Balkarian Scientific Center of RAS<\/em>. 2023. No. 5(115). Pp. 11\u201324. (In Russian)<\/li>\n\n\n\n
  4. Doursat R. Organically grown architectures: Creating decentralized, autonomous systems by embryomorphic engineering. Organic Computing.<\/em> Ed. R.P. W\u00fcrtz, Springer-Verlag. Pp. 167\u2013200.<\/li>\n\n\n\n
  5. Fedor A., Zachar I., Szil\u00e1gyi A. et al. Cognitive Architecture with Evolutionary Dynamics Solves Insight Problem. Front. Psychol. <\/em>2017. Vol. 8.<\/li>\n\n\n\n
  6. Werfel J., Nagpal R. Extended stigmergy in collective construction. IEEE Intelligent Systems. 2006. No. 21(2). Pp. 20\u201328.<\/li>\n\n\n\n
  7. Jinek M., Chilynksi K., Fonfara I. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science.<\/em> 2012. Vol. 337. No. 6069. Pp. 816\u2013821. DOI: 10.1126\/science.1225829<\/li>\n\n\n\n
  8. Nagoev Z.V. Intellektika, ili Myshleniye v zhivykh i iskusstvennykh sistemakh<\/em> [Intelligence, or thinking in living and artificial systems]. Nalchik: Izdatel’stvo KBNTS RAN, 232 p. (In Russian)<\/li>\n\n\n\n
  9. Anchekov M.I., Bzhikhatlov K.Ch., Leshkenov A.M. High-throughput crop phenotyping systems. News of the Kabardino-Balkarian Scientific Center of RAS<\/em>. 2022. No. 5(109). Pp. 19\u201324. (In Russian)<\/li>\n\n\n\n
  10. Nagoev Z.V., Nagoeva O.V. Obosnovaniye simvolov i mul’tiagentnyye neyrokognitivnyye modeli semantiki yestestvennogo yazyka <\/em>[Symbol grounding and multi-agent neurocognitive models of natural language semantics]. Nalchik: Izdatel’stvo KBNTS RAN, 2022. 150 p. (In Russian)<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Information about the authors<\/strong><\/summary>\n
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    Nagoev Zalimkhan Vyacheslavovich<\/strong>, Candidate of Technical Sciences, General Director of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
    360000, Russia, Nalchik, 37-a I. Armand street;
    zaliman@mail.ru, ORCID: https:\/\/orcid.org\/0000-0001-9549-1823
    Anchekov Murat Inusovich<\/strong>, Researcher of the Laboratory \u201cMolecular Breeding and Biotechnology\u201d, Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
    360000, Russia, Nalchik, 224 Kirov street;
    murat.antchok@gmail.com, ORCID: https:\/\/orcid.org\/0000-0002-8977-797X
    Kurashev Zhiraslan Hautievich<\/strong>, Head of the Laboratory \u201cMolecular Breeding and Biotechnology\u201d, Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
    360000, Russia, Nalchik, 224 Kirova street;
    ORCID: https:\/\/orcid.org\/0000-0001-9442-6122
    Khamov Anzor Azamatgerievich<\/strong>, Junior Researcher of the Laboratory \u201cMolecular Breeding and Biotechnology\u201d, Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
    360000, Russia, Nalchik, 224 Kirov street;
    opitnoe2014@mail.ru, ORCID: https:\/\/orcid.org\/0000-0003-3269-4572<\/p>\n\n\n\n

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    Neurocognitive learning algorithm for a multi-agent system for evolutionary modeling of gene expression according to PCR analysis of plants Z.V. Nagoev, M.I. Anchekov, Zh.Kh. Kurashev, A.A. Khamov Abstract. The work is aimed at creating a methodology for using general artificial intelligence systems to manage the process of creating new plant hybrids with a given set […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-10321","page","type-page","status-publish","hentry"],"yoast_head":"\n25.6.19 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/25-6-19-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"25.6.19 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Neurocognitive learning algorithm for a multi-agent system for evolutionary modeling of gene expression according to PCR analysis of plants Z.V. 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Nagoev, M.I. Anchekov, Zh.Kh. Kurashev, A.A. Khamov Abstract. 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