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“NEWS OF THE KABARDINO-BALKARIAN

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The journal was founded in 1998, 6 issues per year
ISSN 1991-6639 (print),

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Energy exchange model between agneurons as part of multi-agent neurocognitive architecture

I.A. Pshenokova, A.Z. Apshev

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Abstract: In recent years distributed artificial intelligence has attracted the attention of scientists due to its ability to solve complex computing problems. The main area of this article is multi-agent systems. The flexibility of multi-agent systems makes them suitable for solving problems in various disciplines, including computer science, economics, civil construction, etc. The aim of this study is to build an imitation model of energy exchange between agents in an intellectual decision-making system based on multi-agent neurocognitive architecture. The object of study is the process of energy exchange in the neural structure of the brain. The work proposes a model of energy exchange between agneurons as part of a multi-agent neurocognitive architecture of an intellectual agent. The proposed formalism is based on the neurofunctional similarity of the agneurons of an intellectual agent with neurons of the human brain. The process of energy exchange and consumption of the brain neurons in the process of performing cognitive functions is considered. In particular, the work combines the knowledge gained as a result of the study of mitochondrial function and the metabolic energy of the brain. Formalism is presented for calculating the energy of agneurons and actors at different levels of the invariant of multi-agent neurocognitive architecture of an intelligent agent. Further work will be to test the presented architecture in the simulation modeling program.

Keywords: intellectual agent, multiagent systems, cognitive architecture, decision making and management systems

For citation. Pshenokova I.A., Apshev A.Z. Energy exchange model between agneurons as part of multi-agent neurocognitive architecture. News of the Kabardino-Balkarian Scientific Center of RAS. 2023. No. 5(115). Pp. 32–40. DOI: 10.35330/1991-6639-2023-5-115-32-40

References
  1. Dorri A., Kanhere S., Jurdak R. Multi-agent systems: A Survey. IEEE Access. 2018. Vol. 6. Pp. 28573–28593. DOI: 10.1109/ACCESS.2018.2831228
  2. Bond A., Gasser L. Readings in distributed artificial intelligence. San Mateo, CA, USA: Morgan Kaufmann, 2014. 668 p.
  3. Wooldridge M. An Introduction to multiagent systems. New York, NY, USA: Wiley, 488 p.
  4. Shamshirband S., Anuar N., Kiah M., Patel A. An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique. Eng. Appl. Artif. Intell. 2013. Vol. 26. Pp. 2105–2127. DOI:10.1016/j.engappai.2013.04.010
  5. Zou A.-M., Kumar K., Hou Z.-G. Distributed consensus control for multi-agent systems using terminal sliding mode and Chebyshev neural networks. Int. J. Robust Nonlinear Control. 2013. Vol. 23(3). Pp. 334–357. DOI: 10.1002/rnc.1829
  6. Calvaresi D. et al. Real-time multi-agent systems: rationality, formal model, and empirical results. Autonomous agents and multi-agent systems. 2021. Vol. 35(1). P. 12. DOI: 10.1007/s10458-020-09492-5.
  7. Zhang D. et al. Physical safety and cyber security analysis of multi-agent systems: A survey of recent advances. IEEE/CAA Journal of automatica sinica. 2021. Vol. 8(2). Pp. 319–333. DOI: 10.1109/JAS.2021.1003820
  8. Rezaee H., Abdollahi F. Average consensus over high-order multiagent systems. IEEE Trans. autom. control. 2015. Vol. 60(11). Pp. 3047–3052. DOI: 10.1109/TAC.2015.2408576
  9. Ma L., Min H., Wang S. et al. An overview of research in distributed attitude coordination control. IEEE/CAA J. autom. sinica. 2015. Vol. 2(2). Pp. 121–133.
  10. Nagoev Z., Pshenokova I., Nagoeva O. et al. Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures. Cognitive systems research. Vol. 66. Pp. 82–88. DOI: 10.1016/j.cogsys.2020.10.015
  11. Nagoev Z.V. Intellektika, ili myshlenie v zhivykh i iskusstvennykh sistemakh [Intellectics, or thinking in natural and artificial systems]. Nal’chik: Izdatel’stvo KBNTS RAN, 2013. 211 p.
  12. Nagoev Z.V. Multi-agent existential mappings and functions. News of the Kabardino-Balkarian Scientific Center of RAS. 2013. No. 4(54). Pp. 63–71.
  13. Nagoev Z., Pshenokova I., Nagoeva O. et al. Situational analysis model in an intelligent system based on multi-agent neurocognitive architectures. Journal of Physics: Conference Series. 2131 (2021) 022103. DOI: 10.1088/1742-6596/2131/2/022103
  14. Picard M., McEwen B.S. Mitochondria impact brain function and cognition. Proceedings of the National Academy of Sciences. 2014. Vol. 111. No. 1. Pp. 7–8.
  15. Wallace D.C. Bioenergetics, the origins of complexity, and the ascent of man. Proceedings of the National Academy of Sciences. 2010. Vol. 107. No. supplement_2. Pp. 8947–8953.
  16. Chan D.C. Fusion and fission: interlinked processes critical for mitochondrial health. Annual Review of genetics. 2012. Vol. 46. Pp. 265–287.
  17. Pshenokova I.A., Nagoeva O.V., Apshev A.Z. et al. Formation of dynamic cause-and-effect relationships in controlling the behavior of an intelligent agent based on the formalism of multiagent neurocognitive architectures. News of the Kabardino-Balkarian Scientific Center of RAS. No. 5 (109). Pp. 73–80. DOI: 10.35330/1991-6639-2022-5-109-73-80
  18. Raichle M.E., Gusnard D.A. Appraising the brain’s energy budget. PNAS. 2002. Vol. 99(16). Pp. 10237–10239. DOI: 10.1073/pnas.172399499
  19. Bruckmaier M., Tachtsidis I., Phan P. e al. Attention and Capacity Limits in Perception: A Cellular Metabolism Account. Journal of Neuroscience. 2020. Vol. 40 (35). Pp. 6801–6811. DOI: 10.1523/JNEUROSCI.2368-19.2020
Information about the authors

Pshenokova Inna Auesovna, Candidate of Physical and Mathematical Sciences, Head of lab., Institute of Computer Science and Problems of Regional Management – branch of 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
Apshev Artur Zaurbievich, Research Assistant, the Institute of Computer Science and Problems of Regional Management – branch of Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
360000, Russia, Nalchik, 37-a I. Armand street;
apshev@mail.ru

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