Decision Making an Autonomous Robot Based on Matrix Solution of Systems of Logical Equations that Describe the Environment of Choice for Situational Control

  • Andrey E. GorodetskiyEmail author
  • Irina L. Tarasova
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 261)


Problem statement: Progress in the development of modern industrial society is associated with the intellectualization of robotic systems, allowing to make informed decisions in group interaction in a dynamic environment of choice. Purpose of research: Providing autonomous robots with the ability to understand the language of sensations to enable decision-making regarding appropriate behavior in a group of autonomous robots under conditions of uncertainty. Results: A method of processing sensory information in the Central nervous system of the robot in order to obtain pragmatic information about the environment of choice is proposed. The methods of decision-making based on pragmatic information using matrix solutions of systems of logical equations in the description of optimization problems in the form of binary relations are described. Practical significance: The possibility of conscious decision-making by an Autonomous robot based on the analysis of the coordinator’s goal of functioning, as well as the behavior and intentions of neighboring robots, the selection of semantic data on the environment pragmatic data related to the purpose of functioning, and the choice of genetic algorithms that most optimally lead to the goal of functioning is shown.


Situational control Groups of autonomous robots Environment of choice Central nervous system of the robot Quantization Fuzzification Images and images Sensory Syntactic Semantic and pragmatic data Deterministic Stochastic and not fully defined constraints Synthesis of the algorithm for finding the optimal solution. Progress in this area is associated with the development of intellectualization of robots Allowing to make informed decisions in group interaction in a dynamic environment of choice 



This work was financially supported by Russian Foundation for Basic Research Grants 18-01-00076 and 19-08-00079.


  1. 1.
    Dobrynin, D.A.: Intelligent robots yesterday, today, tomorrow. In: X natsional’naia konferentsiia po iskusstvennomu intellektu s mezhdunarodnym uchastiem KII-2006 (25–28 sentiabria 2006 g., Obninsk) [X National Conference on Artificial Intelligence with International Participation (25–28 September 2006, Obninsk)]: Conference Proceedings, vol. 2. FIZMATLIT Publ., Moscow (2006). (in Russian)Google Scholar
  2. 2.
    Gorodetskiy, A.E., Tarasova, I.L., Kurbanov, V.G.: Behavioral decisions of a robot based on solving of systems of logical equations. In: Gorodetskiy, A.E., Kurbanov, V.G. (eds.) Smart Electromechanical Systems: The Central Nervous System, 270 p. Springer International Publishing AG (2017). Scholar
  3. 3.
    Gorodetskiy, A.E., Kurbanov, V.G.: Smart Electromechanical Systems: The Central Nervous Systems, 270 p. Springer International Publishing (2017). ISBN 978-3-319-53326-1. Scholar
  4. 4.
    Gorodetskiy, A.E., Kurbanov, V.G., Tarasova, I.L.: Decision making in the central nervous system of the robot. Informatsionno-upravliaiushchie sistemy 1, 21–30 (2018). (in Russian).
  5. 5.
    Gorodetskiy, A., Kurbanov, V., Tarasova, I.: Formation of images based on sensory data of robots. In: Proceedings of the 14th International Conference on Pattern Recognition and Information Processing PRIPT 2019, Minsk, Belarus, 21–23 May 2019Google Scholar
  6. 6.
    Davydov, O.I., Platonov, A.K.: Robot and Artificial Intelligence. Technocratic Approach, no. 112, 24 p. Preprint IPM im. M. V. Keldysh (2017). (in Russian).
  7. 7.
    Krysin, L.P.: Types of pragmatic information in the “explanatory dictionary”. Izvestiya RAN seriya literatury I yazyka 74(2), 3–11 (2015). (in Russian)Google Scholar
  8. 8.
    Meshcheryakov, B.G., Zinchenko, V.P. (eds.): Great Psychological Dictionary. Praym Publ., Moscow (2003)Google Scholar
  9. 9.
    Babich, A.V.: Industrial Robotics, 263 p. Book on Demand Publ., Moscow (2012). (in Russian)Google Scholar
  10. 10.
    Polivtsev, S.A., Khashan, T.S.: The study of geometric and acoustic properties of the sensors for the technical hearing system. Problemy bioniki [Probl. Robots Bionics] 6, 63–69 (2003). (in Russian)Google Scholar
  11. 11.
    Ying, M., Bonifas, A.P., Lu, N., Su, Y., Li, R., Cheng, H., Ameen, A., Huang, Y., Rogers, J.A.: Silicon Nanomembranes for Fingertip Electronics. IOP Publishing Ltd, 10 Aug 2012Google Scholar
  12. 12.
    Gorodetskiy, A.E., Kurbanov, V.G., Tarasova, I.L.: Methods of synthesis of optimal intelligent control systems SEMS. In: Smart Electromechanical Systems, pp. 25–45. http://dx.doi/org/10.1007/978-3-319-27547-5_4Google Scholar
  13. 13.
    Rachkov, M.Yu.: Tekhnicheskie sredstva avtomatizacii [Technical Means of Automation], 185 p. MGIU Publ., Moscow (2009). (in Russian)Google Scholar
  14. 14.
    Zheltikov, M.O.: Osnovy teorii upravleniya [The Basics of Control Theory]. Lecture Notes. SGTU Publ., Samara (2008). (in Russian)Google Scholar
  15. 15.
    Gorodetsky, A.E., Tarasova, I.L.: Nechetkoe matematicheskoe modelirovanie ploho formalizuemyh processov i sistem [Fuzzy Mathematical Modeling Difficult to Formalize Processes and Systems], 336 p. Politekhnicheskii Universitet Publ., Saint-Petersburg (2010). (in Russian)Google Scholar
  16. 16.
    Gorodetskiy, A.: Osnovy teorii intellektual’nykh sistem upravleniia [Foundations of the Theory of Intelligent Control Systems], 313 p. LAP LAMBERT Academic Publishing GmbH @ Co. KG (2011). (in Russian)Google Scholar
  17. 17.
    Karmanov, V.G.: Matematicheskoe programmirovanie [Mathematical Programming], 263 p. Fiz.-Mat. Literature Publ., 2004. (in Russian)Google Scholar
  18. 18.
    Gorodetsky, A.E., Dubarenko, V.V.: Combinatorial method for calculating the probability of complex logic functions. Zhurnal vychislitel’noi matematiki i matematichskoi fiziki 39(7), 1201–1203 (1999). (in Russian)Google Scholar
  19. 19.
    Svetlov, V.A.: Methodological concept of Charles Pearce’s scientific knowledge: unity of abduction, deduction and induction. Logiko-Filosofskie shtudii 5, 165–187 (2008). (in Russian). ISSN 2071-9183Google Scholar
  20. 20.
    Iudin, D.B.: Vychislitel’nye metody teorii priniatiia reshenii [Computational Methods of Decision Theory], 320 p. Nauka Publ., Moscow (1989). (in Russian)Google Scholar
  21. 21.
    Zhegalkin, I.I.: Arifmetizatsiia simvolicheskoi logiki [Arithmetization Symbolic Logic]. Matematicheskii sbornik [Math. Collect.] 35(3–4) (1928). (in Russian)Google Scholar
  22. 22.
    Gorodetskiy, A.E., Dubarenco, V.V., Erofeev, A.A.: Algebraic approach to the solution of logical control problems. Avtomatika i telemekhanika [Autom. Remote Control] 2, 127–138 (2000). (in Russian)Google Scholar

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Authors and Affiliations

  1. 1.Institute of Problems of Mechanical Engineering, Russian Academy of SciencesSt. PetersburgRussia

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