V.A. Golovskoy1

1 Krasnodar Higher Military School (Krasnodar, Russia)

2 golovskoy_va@mail.ru

The intellectualization of information technology systems is an urgent direction in the development of society, and one example is cognitive radio systems with the ability to self-study. The paper examines the features of cognitive radio systems that cause a change in approaches to their modeling and test planning, which is of critical importance for radio systems designed to function in conditions of electronic conflict.

The purpose of the work is to analyze the problems of modeling and forecasting the knowledge-based behavior of cognitive radio systems in order to identify fundamental differences in the content of their testing system.

A hypothesis is formulated about the influence of the ability of unlimited self-learning of cattle on the potential possibility of modeling its behavior in a real non-deterministic environment. The mass forecasting problem inherent in information technology systems with unlimited self-learning abilities has been formalized. To test the hypothesis, a theorem on the algorithmic insolubility of the mass problem of predicting the behavior of cattle based on an unlimited set of generated knowledge, with the non-deterministic nature of the real environment, is formulated and proved. The necessity of developing requirements for the data on which the cattle will be tested is shown. A number of tasks have been proposed that must be solved to ensure effective cattle testing.

The theoretical significance of the work lies in the fact that a massive problem has been formulated that is of interest to researchers involved in the creation of self-learning information technology systems.

A number of tasks proposed as a result of the analysis that should be solved to ensure the testing of cognitive radio systems are of practical importance.

Golovskoy V.A. Analysis of the problems of predicting the behavior of cognitive radio systems. Radio engineering. Radiotekhnika. 2024. V. 88. № 12. P. 134−145. DOI: https://doi.org/10.18127/j00338486-202412-12 (In Russian)

1. Wang P., Gao H., Xiao, C., Guo X., Gao Y., Zi Y. Extended research on the security of visual reasoning CAPTCHA. IEEE Transactions on dependable and secure computing. 2023. URL: https://ieeexplore.ieee.org/document/10023959 (data obrashhenija 29.06.2024).
2. Kaljaev I.A. Kak izmerit' iskusstvennyj intellekt? Iskusstvennyj intellekt i prinjatie reshenij. 2023. № 1. S. 3–11.
3. Li H., Guo D., Fan W., Xu M., Huang J., Meng F., Song Y. Multi-step Jailbreaking Privacy Attacks on ChatGPT / arXiv preprint arXiv:2304.05197. – 2023. URL: https://arxiv.org/pdf/2304.05197.pdf (data obrashhenija 22.06.2024).
4. Alfonseca M., Cebrian M., Anta A.F., Coviello L., Abeliuk A., Rahwan I. Superintelligence cannot be contained: lessons from computability theory. Journal of Artificial Intelligence Research. 2021. № 70. P. 65-76.
5. Gunning D., Aha D.W. DARPA's Explainable Artificial Intelligence (XAI) Program. AI Magazine. 2019. V. 40(2). P. 44–58.
6. Haug S., Marks R. J., Dembski W.A. Exponential Contingency Explosion: Implications for Artificial General Intelligence. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2022. V. 52. № 5. P. 2800–2808.
7. Stefanuk V.L., Zhozhikashvily A.V., Savinitch L.V. Intelligent systems with restricted autonomy. Lecture Notes in Computer Science. 2020. V. 12412 LNAI. P. 460–471.
8. Bajgar O., Horenovsky J. Negative Human Rights as a Basis for Long-term AI Safety and Regulation. Journal of Artificial Intelligence Research. 2023. № 76. P. 1043–1075.
9. Burgin M. Three Approaches to Artificial Intelligence. Proceedings. 2022. V. 81. № 1:147. URL: https://doi.org/10.3390/pro-ceedings2022081147 (data obrashhenija 29.06.2024).
10. Golovskoj V.A. Funkcional'naja model' podsistemy upravlenija resursami kognitivnoj radiosistemy robototehniches-kogo kompleksa. Izvestija JuFU. Tehnicheskie nauki. 2023. № 1(231). S. 241–251 (in Russian).
11. Abrosimov V.K., Gladkij A.V. Intellektual'nost' boevyh svojstv perspektivnyh robototehnicheskih kompleksov nazemnogo bazirovanija. Izvestija Rossijskoj akademii raketnyh i artillerijskih nauk. 2024. № 1(131). S. 109-115 (in Russian).
12. Anan'ev P.P., Plotnikova A.V., Timofeev A.S., Meshherjakov R.V., Beljakov K.O. Problemy testirovanija robototehnicheskih sistem dlja peremeshhenija po kosmicheskim ob#ektam. Robototehnika i tehnicheskaja kibernetika. 2021. T. 9. № 3. S. 180–185.
13. Jharko E., Meshcheryakov R., Promyslov V. Aspects of Nuclear Power Plant Digital Decommissioning. 2021 International Siberian Conference on Control and Communications (SIBCON). Kazan. Russia. 2021. P. 1–6.
14. Orr J., Dutta A. Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey. Sensors. 2023. V. 23. № 7. URL: https://doi.org/10.3390/s23073625 (data obrashhenija 22.06.2024) (in Russian).
15. Osipov V.Ju., Miloserdov D.I. Nejrosetevoe prognozirovanie sobytij dlja robotov s nepreryvnym obucheniem. Informacionno-upravljajushhie sistemy. 2020. № 5. S. 33–42 (in Russian).
16. Turdakov D.Ju., Avetisjan A.I., Arhipenko K.V., Anciferova A.V., Vatolin D.S., Volkov S.S., Gasnikov A.V., Devjatkin D.A., Drobyshevskij M.D., Kovalenko A.P., Krivonosov M.I., Lukashevich N.V., Malyh V.A., Nikolenko S.I., Oseledec I.V., Perminov A.I., Sochenkov I.V., Tihomirov M.M., Fedotov A.N., Hachaj M.Ju. Doverennyj Iskusstvennyj intellekt: vyzovy i perspektivnye reshenija. Doklady Rossijskoj akademii nauk. Matematika, informatika, processy upravlenija. 2022. T. 508. № 1. S. 13–18 (in Russian).
17. Golovskoj V.A. O probleme ogranichennosti pri issledovanijah kognitivnyh radiosistem. Sb. trudov XXIX Mezhdunarodnoj nauch.-tehnich. konf., posvjashhennoj 70-letiju kafedry radiofiziki VGU «Radiolokacija, navigacija, svjaz'» (g. Voronezh, 18–20 aprelja 2023 g.). Voronezh: VGU. 2023. T. 5. S. 283–287 (in Russian).
18. Haigh K.Z., Nguyen T. Challenges of Testing Cognitive EW Systems. 2023 IEEE AUTOTESTCON. National Harbor. MD, USA, 2023. P. 1-8.
19. V'jugin V.V., Trunov V.G. Prognozirovanie lokal'no stacionarnyh dannyh s ispol'zovaniem predskazanij jekspertnyh strategij. Informacionnye processy. 2023. T. 23. № 4. S. 470-487 (in Russian).
20. V'jugin V.V. Matematicheskie osnovy mashinnogo obuchenija i prognozirovanija. Izd. 3-e. MCNMO. 2022. 400 s. (in Russian).
21. Anohin A.O., Parygin D.S., Sadovnikova N.P., Finogeev A.A., Gurtjakov A.S. Modelirovanie povedenija intellektual'nyh agentov na osnove metodov mashinnogo obuchenija v modeljah konkurencii. Programmnye produkty i sistemy. 2023. T. 36. № 1. S. 046–059 (in Russian).
22. Mikoni S.V. Podhod k ocenivaniju urovnja intellektual'nosti informacionnoj sistemy. Ontologija proektirovanija. 2023. T. 13. № 1(47). S. 29-43 (in Russian).
23. Danilov M.S., Golubinskij A.N., Mihajlova I.V. Ocenka tochnosti nejrosetevoj modeli, obuchennoj na sinteticheskih dannyh. Teorija i tehnika radiosvjazi. 2023. № 4. S. 73-78 (in Russian).
24. Shlenskih D.A., Belokopytov M.L., Anohin D.V., Ivanov I.G. Metod sinteza dannyh dlja povyshenija jeffektivnosti obuchenija nejronnyh setej. Zhurnal radiojelektroniki. 2024. № 3. URL: https://doi.org/10.30898/1684-1719.2024.3.8 (data obrashhenija 29.06.2024).
25. Borisov V.I., Vilkov S.V. Tehnologicheskaja platforma razvitija sistem upravlenija, svjazi i radiojelektronnoj bor'by. Teorija i tehnika radiosvjazi. 2023. № 1. S. 5-11 (in Russian).
26. Fernando P., Wei-Kocsis J. Stealthy Adversarial attacks against automated modulation classification in cognitive radio. 2023 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW). Cleveland. OH, USA. 2023. Р. 1-6.
27. Bharti B., Thakur P., Singh G. A framework for spectrum sharing in cognitive radio networks for military applications. IEEE Potentials. 2021. V. 40. № 5. Р. 39-47.
28. Hilal W., Gadsden S.A., Yawney J. Cognitive dynamic systems: A review of theory, applications, and recent advances. Proceedings of the IEEE. 2023. V. 111. № 6. Р. 575--622.
29. Report ITU-R SM.2152. Definitions of Software Defined Radio and Cognitive Radio System. URL: https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-SM.2152-2009-PDF-e.pdf.
30. Andreev G.I., Tihomirov V.A., Zamarin M.E. Problemy iskusstvennogo intellekta v prakticheskoj oblasti radiojelektronnoj bor'by. Radiotehnika. 2024. T. 88. № 5. S. 5-14. DOI: https://doi.org/10.18127/j00338486-202405-01 (in Russian).
31. Zacman I.M. Dannye, informacija i znanie v nauchnoj paradigme informatiki. Informatika i ee primenenija. 2023. T. 17. № 1. S. 116–125 (in Russian).
32. Granichin O.N. Obratnye svjazi, usrednenie i randomizacija v upravlenii i izvlechenii znanij. Stohasticheskaja optimizacija v informatike. 2012. T. 8. № 2. S. 3–48 (in Russian).
33. Kostenko K. I. Sravnenie formalizmov znanij. Intellektual'nye sistemy. Teorija i prilozhenija. 2014. T. 18. № 2. S. 115–132 (in Russian).
34. Baumann R., Strass H. An Abstract, Logical Approach to Characterizing Strong Equivalence in Non-monotonic Knowledge Representation Formalisms. Artificial Intelligence. 2022. V. 305. P. 103680. URL: https://doi.org/10.1016/j.artint.2022.103680 (data obrashhenija 02.07.2024).
35. Vasil'ev S.N. Metod abduktivnogo vyvoda v zadachah ob’jasnenija nabljudaemogo. Izvestija Rossijskoj akademii nauk. Teorija i sistemy upravlenija. 2021. № 1. S. 160–168 (in Russian).
36. Golovskoj V.A. Operacionnaja model' kognitivnoj radiosistemy robototehnicheskogo kompleksa. T-Comm: Telekommunikacii i transport. 2024. T. 18. № 5. S. 12-20 (in Russian).
37. Surov I.A. Zhiznennyj cikl: smyslovaja matrica processnogo modelirovanija. Ontologija proektirovanija. 2022. T. 12. № 4(46). S. 430–453 (in Russian).
38. Harkevich A.A. O cennosti informacii. Problemy kibernetiki. 1960. № 4. S. 53-72 (in Russian).
39. Borisov V.V., Kurilin S.P., Luferov V.S. Nechetkie reljacionnye kognitivnye temporal'nye modeli dlja analiza i prognozirovanija sostojanija slozhnyh tehnicheskih sistem. Prikladnaja informatika. 2022. T. 17. № 1(97). S. 27-38 (in Russian).
40. Pavlenko E.Ju. Algoritm predskazanija svjazej v samoregulirujushhejsja seti s adaptivnoj topologiej na baze teorii grafov i mashinnogo obuchenija. Modelirovanie i analiz informacionnyh sistem. 2023. T. 30. № 4. S. 288-307 (in Russian).
41. Kuz'min E.V., Sokolov V.A., Chalyj D.Ju. Problemy ogranichennosti schetchikovyh mashin Minskogo. Programmirovanie. 2010. T. 36. № 1. S. 5–15 (in Russian).
42. Golovskoj V.A. Rasshirenie modeli slozhnogo radiojelektronnogo konflikta. Sb. trudov XXX Mezhdunar. nauch.-tehnich. konf. «Radiolokacija, navigacija, svjaz'» (g. Voronezh, 16–18 aprelja 2024 g.). 2024. T. 5. S. 63-68 (in Russian).
43. Satton R.S., Barto Je.Dzh. Obuchenie s podkrepleniem: Vvedenie. Per. s angl. A.A. Slinkina. Izd. 2-e. M.: DMK Press. 2020. 552 s.
44. Golovskoj V.A. Matematicheskaja model' funkcionirovanija kognitivnoj radiosistemy. Zhurnal radiojelektroniki. 2024. № 3. https://doi.org/10.30898/1684-1719.2024.3.4 (in Russian).