{"id":2299,"date":"2025-05-14T08:35:00","date_gmt":"2025-05-14T07:35:00","guid":{"rendered":"http:\/\/newskbncran.ru\/?page_id=2299"},"modified":"2026-03-27T10:37:22","modified_gmt":"2026-03-27T10:37:22","slug":"26-6-17-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/26-6-17-en\/","title":{"rendered":"26.6.17 en"},"content":{"rendered":"\n

Universal expert system based on ontoepisosociophylogenetic training of federations of intelligent neurocognitive agents<\/strong><\/h1>\n\n\n\n

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

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Abstract.<\/em><\/strong> <\/strong>The work is devoted to solve a scientific problem of developing a conceptual justification for the possibility of autonomous training of intelligent expert systems based on ontoepisociophylogenetic training of neurocognitive agents. The aim of the study is to develop basic principles of creating universal expert systems based on ontoepisociophylogenetic training of federated intelligent neurocognitive agents. The basic principles of ontoepisociophylogenetic training of universal federated expert systems have been developed. It is shown that the functional specialization of intelligent agents within a federation, subject to their cooperation in order to maximize the combined increment of the values \u200b\u200bof the target functions, allows overcoming efficiency limitations. The use of epigenetic algorithms for fixing ontological knowledge of intelligent agents within a federation in generations of evolutionary optimization is substantiated. The possibility of constructing multi-generational populations in order to increase the overall efficiency of a universal expert federated system is substantiated.<\/p>\n\n\n\n

Keywords:<\/em><\/strong> artificial intelligence, multi-agent systems, neurocognitive architectures, ontoepisociophylogenetic algorithms, machine learning, universal expert systems<\/p>\n\n\n\n

For citation<\/strong>. Nagoev Z.V., Anchekov M.I., Kurashev Zh.Kh., Nagoeva O.V., Pshenokova I.A., Khamov A.A. Universal expert system based on ontoepisosociophylogenetic training of federations of intelligent neurocognitive agents.  News  of  the  Kabardino-Balkarian  Scientific  Center  of  RAS.<\/em>  <\/strong>2024.  Vol. 26.  No. 6.  Pp. 197\u2013207. DOI: 10.35330\/<\/strong>1991-6639-2024-26-6-197-207<\/p>\n\n\n\n

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  1. Nagoev Z.V., Anchekov M.I., Bzhikhatlov K.Ch., Nagoeva O.V., Pshenokova I.A. Ontoepisociophylogenetic development of general artificial intelligence systems based on multi-agent neurocognitive architectures. News of the Kabardino-Balkarian Scientific Center of RAS. No. 6(110). Pp. 61\u201375. (In Russian)<\/li>\n\n\n\n
  2. Nagoev Z.V., Anchekov M.I., Apshev A.Z., Bzhikhatlov K.Ch., Kankulov S.A., Nagoeva O.V., Pshenokova I.A., Khamov A.A., Enes A.Z. Formal model of the genome of an agent of general artificial intelligence based on multi-agent neurocognitive architectures. News of the Kabardino-Balkarian Scientific Center of RAS. 2023. No. 5(115). Pp. 11\u201324. (In Russian)<\/li>\n\n\n\n
  3. Nagoev Z.V. Intellektika, ili Myshleniye v zhivykh i iskusstvennykh sistemakh [Intellectics, or Thinking in Living and Artificial Systems]. Nalchik: Izdatel’stvo KBNTS RAN, 2013. 235 p. (In Russian)<\/li>\n\n\n\n
  4. Nagoev Z.V., Bzhikhatlov K.Ch., Zagazezheva O.Z. Neurocognitive methods and algorithms for federated learning of intelligent integrated information and control systems in a real communication environment. Izvestiya YUFU. Tekhnicheskiye nauki [Bulletin of the Southern Federal University. Engineering Sciences]. 2024. No. 1(237). Pp. 111\u2013121. (In Russian)<\/li>\n\n\n\n
  5. Nagoev Z.V., Nagoeva O.V. Obosnovaniye simvolov i mul’tiagentnyye neyrokognitivnyye modeli semantiki yestestvennogo yazyka [Symbol Grounding and Multi-Agent Neurocognitive Models of Natural Language Semantics]. Nalchik: Izdatel’stvo KBNTS RAN, 2022. 150 p. (In Russian)<\/li>\n\n\n\n
  6. Abazokov M.A., Anchekov M.I., Bzhikhatlov K.Ch., Kurashev Zh.Kh., Nagoev Z.V., Nagoeva O.V., Unagasov A.A., Khamov A.A. Analysis of the computational complexity of federated algorithms for neurocognitive control of simulation phenogenetic models of plan ts (metadata). News of the Kabardino-Balkarian Scientific Center of RAS. 2024. No. 5(121). Pp. 107\u2013129. (In Russian)<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Information about the author<\/strong><\/summary>\n
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    Zalimkhan V. Nagoev<\/strong>, Candidate of Engineering 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, SPIN-code: 6279-5857
    Murat I. Anchekov<\/strong>, Researcher of the Laboratory of Molecular Breeding and Biotechnology,
    Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
    360000, Russia, Nalchik, 37-a I. Armand street;
    murat.antchok@gmail.com, ORCID: https:\/\/orcid.org\/0000-0002-8977-797X, SPIN-code: 3299-0927
    Zhiraslan Kh. Kurashev<\/strong>, Head of the Laboratory of Molecular Breeding and Biotechnology,
    Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
    360000, Russia, Nalchik, 224 Kirov street;
    ORCID: https:\/\/orcid.org\/0000-0001-9442-6122, SPIN-code: 8549-2620
    Olga V. Nagoeva<\/strong>, Researcher of the Department of Multiagent Systems, Institute of Computer Science
    and Problems of Regional Management \u2013 branch of Kabardino-Balkarian Scientific Center of the Russian
    Academy of Sciences;
    360000, Russia, Nalchik, 37-a I. Armand street;
    nagoeva_o@mail.ru, ORCID: https:\/\/orcid.org\/0000-0003-2341-7960, SPIN-code: 9478-3325
    Inna A. Pshenokova<\/strong>, Candidate of Physical and Mathematical Sciences, Senior Research of the
    Laboratory of Molecular Breeding and Biotechnology, Kabardino-Balkarian Scientific Center of the
    Russian Academy of Sciences;
    360000, Russia, Nalchik, 37-a I. Armand street;
    pshenokova_inna@mail.ru, ORCID: https:\/\/orcid.org\/0000-0003-3394-7682, SPIN-\u043a\u043e\u0434: 3535-2963
    Anzor A. Khamov<\/strong>, Junior Researcher of the Laboratory of Molecular Breeding and Biotechnology,
    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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    Universal expert system based on ontoepisosociophylogenetic training of federations of intelligent neurocognitive agents Z.V. Nagoev, M.I. Anchekov, Zh.Kh. Kurashev, O.V. Nagoeva, I.A. Pshenokova, A.A. Khamov Upload the full text Abstract. The work is devoted to solve a scientific problem of developing a conceptual justification for the possibility of autonomous training of intelligent expert systems based […]<\/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-2299","page","type-page","status-publish","hentry"],"yoast_head":"\n26.6.17 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\/26-6-17-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"26.6.17 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=\"Universal expert system based on ontoepisosociophylogenetic training of federations of intelligent neurocognitive agents Z.V. 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Nagoev, M.I. Anchekov, Zh.Kh. Kurashev, O.V. Nagoeva, I.A. Pshenokova, A.A. Khamov Upload the full text Abstract. 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