Concepts of conducting power competitions of specialized systems and the possibility of their modeling

Cover Page

Cite item

Full Text

Abstract

Background. The complexity of designing and operating specialized organizational and technical systems requires the creation of models of their functioning. One of the problems in creating adequate models is the fairly frequent change of concepts of force collisions. It is necessary to consider the principles of technology that allows adapting the created models to one or another concept. Materials and methods. By introducing specific operations on systems and their elements, an expanded definition of specialized organizational and technical systems is given. Descriptions of the most well-known concepts are formalized and the main factors determining the requirements for the technology of adapt-ing models to concepts are analyzed. Results. A description of the technology is given that allows combining the functions of the control subsystem, the operation of special software and a simulation model of the activity of agents of specialized systems. Conclusions. The proposed technology allows adapting the developed simulation models to the described concepts, and makes it possible to create software for simulation models in the process of creating software and information support for specialized systems.

About the authors

Mikhail Yu. Babich

Research and Production Enterprise “Rubin”

Author for correspondence.
Email: babichmj@mail.ru

Doctor of engineering sciences, associate professor, key specialist of the Scientific and Technical Center

(2 Baydukova street, Penza, Russia)

Andrey M. Babich

Research and Production Enterprise “Rubin”

Email: fieryeye@yandex.ru

Candidate of engineering sciences, software engineer of the Scientific and Technical Center,

(2 Baydukova street, Penza, Russia)

References

  1. Solov'ev I.V. Issues of research of complex organizational and technical system. Vestnik MGTU MIREA = Bulletin of MIREA – Russian Technological University. 2013;(1):20‒40. (In Russ.)
  2. Chumichkin A.A. Modeling of automated control systems for complex organizational and technical systems. i-methods. 2020;12(1). (In Russ.). Available at: http://intechspb.com/i-methods/ (accessed 30.03.2025).
  3. Brodskiy Yu.I., Lebedev V.Yu., Ogaryshev V.F., Pavlovskiy Yu.N., Savin G.I. Obshchie problemy modelirovaniya slozhnykh organizatsionno-tekhnicheskikh sistem = General problems of modeling complex organizational and technical systems. Available at: https://ras.ru>ph> 2H0UZEKI.pdf (accessed 18.04.2025).
  4. Beketov S.M., Zubkova D.A., Red'ko S.G. Comparison of optimization methods in simulation models of complex organizational and technical systems. Mode-lirovanie, optimizatsiya i informatsionnye tekhnologii = Modeling, optimization and information technology. 2024;(12):1–12. (In Russ.). doi: 10.26102/2310-6018/2024.46.3.027 Available at: https://moitvivt.ru/ru/journal/pdf?id=1665 (accessed 30.03.2025).
  5. Kostogryzov A.I. Nistratov A.A. Methodological provisions for probabilistic forecasting of the quality of functioning of systems. Pravovaya informatika = Legal informatics. 2024;(3):13–31. (In Russ.)
  6. Prozorov D.E., Pletnev K.E., Yashina A.G. A posteriori estimation of states of a multiply connected Markov chain. Informatsiya i kosmos = Informationa and space. 2016;(1):46–53. (In Russ.)
  7. Zheleznyakov A.O., Zhilin R.A. Modeling of the functioning processes of an organizational and technical system based on Markov random processes. Vestnik Dagestanskogo gosudarstvennogo tekhnicheskogo universiteta. Tekhnicheskie nauki = Bulletin of the Dagestan State Technical University. Engineering sciences. 2024;51(4):71–77. (In Russ.)
  8. Baskov O.V., Nogin V.D. Second-order fuzzy sets and their application in decision making. General concepts. Iskusstvennyy intellekt i prinyatie resheniy = Artificial intelligence and decision making. 2021;(1):3–14. (In Russ.)
  9. Guseynzade Sh.S. Modeling of intelligent control systems using modified fuzzy colored Petri nets. Vestnik komp'yuternykh i informatsionnykh tekhnologiy = Bulletin of computer and information technologies. 2020;17(10):30–37. (In Russ.)
  10. Hans P. Geering. Introduction to Fuzzy Control. Available at: https://www.researchgate.net/publication/259197110 (accessed 30.03.2025).
  11. Zamyatin N.V., Medyantsev D.V. Methodology of neural network modeling of complex systems. Izvestiya Tomskogo politekhnicheskogo universiteta = Proceedings of Tomsk Polytechnic University. 2006;309(8):100–106. (In Russ.)
  12. Sholokhova A.A., Ivanov A.N. Modeling of dynamic systems based on polynomial neural networks. Modelirovanie, optimizatsiya i informatsionnye tekhnologii. Nauchnyy zhurnal = Modeling, optimization and information technology. Scientific journal. 2017;(4). (In Russ.). Available at: http://moit.vivt.ru/ (accessed 30.03.2025).
  13. Gorodetskiy V.I. Multi-agent systems: current state of research and application prospects. Novosti iskusstvennogo intellekta = Artificial intelligence news. 1996;(1):44–59. (In Russ.)
  14. Melekhin V.B., Khachumov M.V. Planning collective activities of autonomous intelligent agents under uncertainty. Iskusstvennyy intellekt i prinyatie resheniy = Artificial intelligence and decision making. 2020;(4):101–113. (In Russ.)
  15. Simankov V.S., Dubenko Yu.V. Systems analysis in hierarchical intelligent multi-agent systems. Vestnik komp'yuternykh i informatsionnykh tekhnologiy = Bulletin of computer and information technologies. 2021;18(3):33–46. (In Russ.). doi: 10.14489/vkit.2021.03.pp.033-046
  16. Listopad S.V. Characteristics and logical structure of the methodology for constructing reflexive-active systems of artificial heterogeneous intelligent agents. Sistemy i sredstva informatiki = Computer science systems and tools. 2023;33(4):18–27. (In Russ.)
  17. Michael E. Bratman. Intentions, Plans, and Practical Reason. Harvard University Press, 1987:224.
  18. Hanen Lejmi-Riahi, Fahem Kebair, Lamjed Ben Said. Agent Decision-Making under Uncertainty: Towards a New E-BDI Agent Architecture Based on Immediate and Expected Emotions. International Journal of Computer Theory and Engineering. 2014;6(3):254–259.
  19. Russell J.A. A Circumflex Model of Affect. Journal of Personality and Social Psychology. 1980;39(6):1161–1178.
  20. Charalampos Karyotisb, Faiyaz Doctorb, Rahat Iqbalb, Anne Jamesb, Victor Changa. A fuzzy computational model of emotion for cloud based sentiment analysis. Available at: https://pure.coventry.ac.uk/ws/portalfiles/ portal/13277719/1_s2.0_S0020025517304164_main.pdf (accessed 02.05.2024).
  21. Chie Hieida, Takato Horii, Takayuki Nagai. Deep Emotion: A Computational Model of Emotion Using Deep Neural Networks. Available at: https://arxiv.org/pdf/1808.08447.pdf (accessed 02.05.2024).
  22. Babich M.Yu., Kuznetsov V.E., Chigirev M.A., Babich A.M. Emotional agents in modeling the functioning of complex organizational and technical systems (part 1). Izvestiya vysshikh uchebnykh zavedeniy. Povolzhskiy region. Tekhnicheskie nauki = University proceedings. Volga region. Engineering sciences. 2024;(4):28–38. (In Russ.). doi: 10.21685/2072-3059-2024-4-3
  23. Krasovskiy A.A. Issues of physical control theory. Avtomatika i telemekhanika = Automation and telemechanics. 1990;(11):3–28. (In Russ.)
  24. Filimonov N.B. The methodological crisis of the “all-conquering mathematization” of modern management theory. Mekhatronika, avtomatizatsiya, upravlenie = Mechatronics, automation, control. 2016;17(5):291–299. (In Russ.)
  25. Bundy J., Michael D. Pfarrer, Cole E. Short, W. Timothy Coombs. Crises and Crisis Management: Integration, Interpretation, and Research Development. Journal of Management. 2017;43(6):1661–1692.
  26. Vorob'ev I.N. Once again about military futurology. Voennaya mysl' = Military thought. 2020;(5):51–57. (In Russ.)
  27. Korzhevskiy A.S., Makhnin V.L. Methodological approaches to forecasting in the sphere of military security of the state. Voennaya mysl' = Military thought. 2022;(5):21– 31. (In Russ.)
  28. Baranovskiy V.G., Kobrinskaya I.Ya., Utkin S.V., Frumkin B.E. The method of situational analysis as a tool for current forecasting in the conditions of transformation of the world order. Vestnik MGIMO-Universiteta = Bulletin of MGIMO University. 2019;(12):7–23. (In Russ.)
  29. Mohammed Ali, Trevor Wood-Harper. Artifcial Intelligence (AI) as a Decision-Making Tool to Control Crisis Situations. Available at: https://www.researchgate.net/publication/358754137_Artificial_Intelligence_AI_as_a_ Decision-Making_Tool_to_Control_Crisis_Situations (accessed 03.08.2024).
  30. Babich M.Yu., Babich A.M. Nonlinearity, irrationality, emotional states in complex specialized systems. Problemy informatiki v obrazovanii, upravlenii, ekonomike i tekhnike: po materialam XXIV Mezhdunar. nauch.-tekhn. konf. = Issues of informatics in education, management, economics and technology: proceedings of the 24th International scientific and engineering conference. Penza: Izd-vo PGU, 2024:3–10. (In Russ.)
  31. Vdovin A.V., Kostin K.K. Artificial intelligence technology in decision support systems – possible approaches and implementation paths. Vestnik akademii voennykh nauk = Bulletin of the Academy of Military Sciences. 2022;(4):91–97. (In Russ.)
  32. Sayapin O.V, Tikhanychev O.V., Bezvesil'naya A.A., Chiskidov S.V. On one trend in the development of algorithms implemented in decision support systems. Programmnye produkty i sistemy = Software products and systems. 2023;36(3):388–397. (In Russ.). doi: 10.15827/0236-235X.142.388–397
  33. Maslennikov O.V., Aliev F.K., Bespalov S.A., Mitroshin E.S. On the computational complexity of modern military tasks. Voennaya mysl' = Military thought. 2023;(2):72– 85. (In Russ.)
  34. Ishechkin B.B., Ishechkin I.B., Evtikhov S.V. Prospects for the application of artificial intelligence in troop management. Voennaya mysl' = Military thought. 2023;(8):79–84. (In Russ.)
  35. Prokaev A.N., Shabunin A.A. Domestic and foreign experience in quantitative justification of decisions in the field of application of naval forces (troops). Voennaya mysl' = Military thought. 2024;(2):77–91. (In Russ.)
  36. Churkin I.P. Methodological analysis of the role of mathematical modeling in decisionmaking on armed struggle in the air sphere. Voennaya mysl' = Military thought. 2022;(6):53–60. (In Russ.)
  37. Babich M.Yu., Babich A.M. The influence of axioms of belonging of agents to several organizational and technical systems on the rational behavior of agents. Iskusstvennye obshchestva = Artificial societies. 2021;16(1). doi: 10.18254/S207751800013885-2 Available at: https://artsoc.jes.su/S207751800013885-2-1 (accessed 30.01.2025).
  38. Ulanov A.S. Predictive assessment of trends in the development of means of armed struggle and methods of their application in future wars. Voennaya mysl' = Military thought. 2022;(8):37–50. (In Russ.)
  39. Smolovoy A.V. Military conflicts of the future: a modern view. Vestnik akademii voennykh nauk = Bulletin of the Academy of Military Sciences. 2022;(3):S. 80–87. (In Russ.)
  40. Malyshev A.I., Mardusin V.N., Khakhalev V.Yu. Analysis of the transformation of the main categories of military conflictology in the doctrinal foundations of the Russian Federation. Voennaya mysl' = Military thought. 2023;(8):6–15. (In Russ.)
  41. Bartosh A.A. Escalation patterns of modern military conflicts. Voennaya mysl' = Military thought. 2024;(1):22–36. (In Russ.)
  42. Tanenya O.S., Vdovin A.V. Transformation of armed confrontation: the conditionality of a new trend in military art. Voennaya mysl' = Military thought. 2024;(3):38–45. (In Russ.)
  43. Kondrat'ev A.E. General characteristics of network architectures used in the implementation of promising network-centric concepts of leading foreign countries. Voennaya mysl' = Military thought. 2008;(12):63–74. (In Russ.)
  44. Polyakov I.V. Justification of requirements for robotic systems intended for engineering reconnaissance. Vestnik akademii voennykh nauk = Bulletin of the Academy of Military Sciences. 2021;(1):103–108. (In Russ.)
  45. Polovinkin V. The first to bloom is “Oreshnik”. Zashchita i bezopasnost' = Security and safety. 2024;4:3–5. (In Russ.)
  46. Babich M.Yu., Kuznetsov V.E., Babich A.M. Simulation of the features of automated control systems for law enforcement agencies in the process of modeling their functioning. i-methods. 2022;14(4). Available at: http://intech-spb.com/wpcontent/uploads/archive/2022/4/1-babich4-2022.pdf (accessed 07.02.2025).
  47. Babich M.Yu., Kuznetsov V.E., Chigirev M.A., Polzunov N.V. Ontology of technology for including the method of staging into the functioning of specialized competing systems. XXI vek: itogi proshlogo i problemy nastoyashchego plyus = The 21st century: results of the past and problems of the present plus. 2023;12(4):12–18. (In Russ.)

Supplementary files

Supplementary Files
Action
1. JATS XML


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».