Skip to main content

Dynamic Coordination of Strategies for Multi-agent Systems

  • Conference paper
  • First Online:
Lecture Notes in Computational Intelligence and Decision Making (ISDMCI 2020)

Abstract

Dynamic coordination of multi-agent systems (MAS) strategies under uncertainty based on a stochastic game model is solved. Dynamic coordination is teaching of the system of generating spatially distributed periodic signals. A stochastic game model is built, criteria for dynamic coordination of player strategies are determined, a recurrent method, algorithmic and software for stochastic game solving are developed. The developed model, method and algorithm for stochastic game resolution provide dynamic MAS coordination, which is manifested in locally deter-mined spatial and temporal alignment of players’ strategies. Dynamic coordination is ensured by an adaptive search method for resolving stochastic play, taking into account current penalties for relevant spatial coordination and rhythm disturbances. It is established that the effectiveness of training players to perform coordinated rhythmic actions is determined by the balance between these penalties, which is achieved by the influence of white noise. Dynamic coordination of agent strategies is achieved as the real-time stochastic game is unleashed based on gathering current information and its adaptive processing. The considered method belongs to the class of reactive methods and simulates the reflexive behavior of living organisms. The method allows finding stochastic game solutions in pure strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Anisimova, O., Vasylenko, V., Fedushko, S.: Social networks as a tool for a higher education institution image creation. In: CEUR Workshop Proceedings, vol. 2392, pp. 54–65 (2019)

    Google Scholar 

  2. Antonyuk, N., Medykovskyy, M., Chyrun, L., Dverii, M., Oborska, O., Krylyshyn, M., Vysotsky, A., Tsiura, N., Naum, O.: Online tourism system development for searching and planning trips with user’s requirements. In: Advances in Intelligent Systems and Computing IV. vol. 1080, pp. 831–863. Springer (2020). https://doi.org/10.1007/978-3-030-33695-0_55

  3. Babichev, S., Korobchynskyi, M., Lahodynskyi, O., et al.: Development of atechnique for the reconstruction and validation of gene network models basedon gene expression profiles. Eastern-Eur. J. Enterp. Technol. 1(4–91), 19–32 (2018). https://doi.org/10.15587/1729-4061.2018.123634

    Article  Google Scholar 

  4. Babichev, S., Lytvynenko, V., Korobchynskyi, M., Taif, M.A.: Objective clustering inductive technology of gene expression sequences features, vol. 716, pp. 359–372 (2017). https://doi.org/10.1007/978-3-319-58274-0_29

  5. Berko, A., Aliekseyeva, K.: Quality evaluation of information resources in web-projects. Actual Prob. Econ. 136(10), 226–234 (2012)

    Google Scholar 

  6. Boreiko, O., Teslyuk, V., Zelinskyy, A., Berezsky, O.: Development of models and means of the server part of the system for passenger traffic registration of public transport in the “smart" city. Eastern-Eur. J. Enterp. Technol. 1(2–85), 40–47 (2017). https://doi.org/10.15587/1729-4061.2017.92831

    Article  Google Scholar 

  7. Borovska, T., Grishin, D., Kolesnik, I., Severilov, V., Stanislavsky, I., Shestakevych, T.: Research and development of models and program for optimal product line control. In: Advances in Intelligent Systems and Computing IV, vol. 1080, pp. 186–201 (2020). https://doi.org/10.1007/978-3-030-33695-0_14

  8. Bublyk, M., Rybytska, O., Karpiak, A., Matseliukh, Y.: Structuring the fuzzy knowledge base of the it industry impact factors. In: Computer Sciences and Information Technologies (CSIT), pp. 21–24 (2018). https://doi.org/10.1109/STC-CSIT.2018.8526760

  9. Bublyk, M., Rybytska, O.: The model of fuzzy expert system for establishing the pollution impact on the mortality rate in Ukraine. In: Computer Sciences and Information Technologies (CSIT), vol. 1, pp. 253–256 (2017). https://doi.org/10.1109/STC-CSIT.2017.8098781

  10. Byrski, A., Kisiel-Dorohinicki, M.: Evolutionary Multi-Agent Systems: From Inspirations to Applications. Springer, Heidelberg (2017)

    Book  Google Scholar 

  11. Chen, B.S.: Stochastic Game Strategies and Their Applications. CRC Press, Boca Raton (2019)

    Book  Google Scholar 

  12. Cherednichenko, O., Vovk, M., Yanholenko, O., Yakovleva, O.: Towards the technology of employers’ requirements collection development. In: Integrated Computer Technologies in Mechanical Engineering, pp. 228–239. Springer (2020). https://doi.org/10.1007/978-3-030-37618-5_21

  13. Chukhray, N., Shakhovska, N., Mrykhina, O., Bublyk, M., Lisovska, L.: Consumer aspects in assessing the suitability of technologies for the transfer. In: Computer Sciences and Information Technologies (CSIT), pp. 142–147 (2019). https://doi.org/10.1109/STC-CSIT.2019.8929879

  14. Chukhray, N., Shakhovska, N., Mrykhina, O., Bublyk, M., Lisovska, L.: Methodical approach to assessing the readiness level of technologies for the transfer. In: Advances in Intelligent Systems and Computing IV, vol. 1080, pp. 259–282 (2020). https://doi.org/10.1007/978-3-030-33695-0_19

  15. Demchuk, A., Lytvyn, V., Vysotska, V., Dilai, M.: Methods and means of web content personalization for commercial information products distribution. In: Advances in Intelligent Systems and Computing, vol. 1020, pp. 332–347 (2020). https://doi.org/10.1007/978-3-030-26474-1_24

  16. Dignum, F., Bradshaw, J., Silverman, B., Doesburg, W.: Agent for Games and Simulations: Trends in Techniques. Concepts and Design. Springer, Heidelberg (2009)

    Book  Google Scholar 

  17. Fudenberg, D., Levine, D.: The Theory of Learning in Games. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  18. Greenwald, A., Hall, K.: Correlated Q-learning. In: Proceedings of the Twentieth International Conference on Machine Learning, pp. 242–249 (2003)

    Google Scholar 

  19. Hu, J., Wellman, M.P.: Nash Q-learning for general-sum stochastic games. Mach. Learn. Res. 4, 1039–1069 (2003)

    MathSciNet  MATH  Google Scholar 

  20. Kaelbling, L., Littman, M., Moore, A.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)

    Article  Google Scholar 

  21. Kanishcheva, O., Cherednichenko, O., Sharonova, N.: Image tag core generation. In: CEUR Workshop Proceedings, pp. 35–44 (2019)

    Google Scholar 

  22. Kazarian, A., Teslyuk, V., Tsmots, I., Tykhan, M.: Implementation of the face recognition module for the “smart” home using remote server. In: Advances in Intelligent Systems and Computing III, AISC, vol. 871, pp. 17–27. Springer (2019). https://doi.org/10.1007/978-3-030-01069-0_2

  23. Komar, M., Golovko, V., Sachenko, A., Bezobrazov, S.: Development of neural network immune detectors for computer attacks recognition and classification. In: International Conference on Intelligent Data Acquisition and Advanced Computing Systems, pp. 665–668 (2013). https://doi.org/10.1109/IDAACS.2013.6663008

  24. Kravets, P.: The methodology of multi-agent systems: a modern state and future trends. In: Proceedings of the International Conference on Computer Science and Information Technologies, CSIT, pp. 125–127 (2006)

    Google Scholar 

  25. Kravets, P.: The control agent with fuzzy logic. In: Perspective Technologies and Methods in MEMS Design, MEMSTECH, pp. 40–41 (2010)

    Google Scholar 

  26. Kravets, P.: Game method for coalitions formation in multi-agent systems. In: Proceedings of the IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2018, vol. 1, pp. 1–4 (2018). https://doi.org/10.1109/STC-CSIT.2018.8526610

  27. Kravets, P., Burov, Y., Lytvyn, V., Vysotska, V.: Gaming method of ontology clusterization. Webology 16(1), 55–76 (2019)

    Article  Google Scholar 

  28. Kushner, H., Yin, G.: Stochastic Approximation and Recursive Algorithms and Applications. Springer, Heidelberg (2013)

    MATH  Google Scholar 

  29. Lee, Y., Chong, Q.: Multi-agent systems support for community-based learning. Interact. Comput. 15(1), 33–55 (2003). https://doi.org/10.1016/S0953-5438(02)00057-7

    Article  Google Scholar 

  30. Lozynska, O., Savchuk, V., Pasichnyk, V.: Individual sign translator component of tourist information system. In: Advances in Intelligent Systems and Computing IV, vol. 1080, pp. 593–601. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-33695-0_40

  31. Nazin, A., Poznyak, A.: Adaptive Choice of Variants: Recurrence Algorithms (in Russian). Science, Moscow (1986)

    Google Scholar 

  32. Neogy, S., Bapat, R., Dubey, D.: Mathematical Programming and Game Theory. Springer, Heidelberg (2018)

    Book  Google Scholar 

  33. Paliy, I., Turchenko, V., Koval, V., Sachenko, A., Markowsky, G.: Approach to recognition of license plate numbers using neural networks. In: IEEE International Conference on Neural Networks – Conference Proceedings, pp. 2965–2970 (2004). https://doi.org/10.1109/IJCNN.2004.1381137

  34. Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York (2005)

    MATH  Google Scholar 

  35. Radley, N.: Multi-Agent Systems – Modeling, Control, Programming, Simulations and Applications. Scitus Academics LLC (2017)

    Google Scholar 

  36. Rusyn, B., Pohreliuk, L., Rzheuskyi, A., Kubik, R., Ryshkovets, Y., Chyrun, L., Chyrun, S., Vysotskyi, A., Fernandes, V.: The mobile application development based on online music library for socializing in the world of bard songs and scouts’ bonfires. In: Advances in Intelligent Systems and Computing IV, vol. 1080, pp. 734–756. Springer (2020). https://doi.org/10.1007/978-3-030-33695-0_49

  37. Rusyn, I.B., Hamkalo, K.R.: Bioelectricity production in an indoor plant-microbial biotechnological system with Alisma plantago-aquatica. Acta Biol. Szeged. 62(2), 170–179 (2019). https://doi.org/10.14232/abs.2018.2.170-179

    Article  Google Scholar 

  38. Rusyn, I., Valko, B.: Container landscaping with festuca arundinaceae as a minibioelectrical systems in a modern buildings. Int. J. Energy Clean Environ. 20(3), 211–219 (2019). https://doi.org/10.14232/abs.2018.2.170-179

    Article  Google Scholar 

  39. Rzheuskyi, A., Kutyuk, O., Voloshyn, O., Kowalska-Styczen, A., Voloshyn, V., Chyrun, L., Chyrun, S., Peleshko, D., Rak, T.: The intellectual system development of distant competencies analyzing for it recruitment. In: Advances in Intelligent Systems and Computing IV, vol. 1080, pp. 696–720. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-33695-0_47

  40. Sachenko, A., Kochan, V., Turchenko, V., Tymchyshyn, V., Vasylkiv, N.: Intelligent nodes for distributed sensor network. In: Conference Record - IEEE Instrumentation and Measurement Technology Conference, pp. 1479–1484 (1999). https://doi.org/10.1109/IMTC.1999.776072

  41. Sachenko, S., Pushkar, M., Rippa, S.: Intellectualization of accounting system. In: IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Dortmund, Germany, 6–8 September 2007, pp. 536–538 (2007). https://doi.org/10.1109/IDAACS.2007.4488477

  42. Scerri, P., Vincent, R., Mailler, R.: Coordination of Large-Scale Multiagent Systems. Springer, Heidelberg (2010)

    MATH  Google Scholar 

  43. Sun, Z.: Cooperative Coordination and Formation Control for Multi-Agent Systems. Springer, Heidelberg (2018)

    Book  Google Scholar 

  44. Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press (1998). https://doi.org/10.1017/S0263574799271172

  45. Tkachenko, V., Cherednichenko, O., Godlevskyi, M.: The concept of device meta-model for real-time communication in the transboundary environment monitoring system. In: International Scientific-Practical Conference,. PIC S&T, pp. 64–70 (2018). https://doi.org/10.1109/INFOCOMMST.2018.8632028

  46. Ummels, M.: Stochastic Multiplayer Games: Theory and Algorithms. Amsterdam University Press, Amsterdam (2014)

    Google Scholar 

  47. Ungureanu, V.: Pareto-Nash-Stackelberg Game and Control Theory: Intelligent Paradigms and Applications. Springer, Heidelberg (2018)

    Book  Google Scholar 

  48. Varetskyy, Y., Rusyn, B., Molga, A., Ignatovych, A.: A new method of fingerprint key protection of grid credential. In: Advances in Intelligent and Soft Computing, pp. 99–103 (2010). https://doi.org/10.1007/978-3-642-16295-4_11

  49. Veres, O., Rusyn, B., Sachenko, A., Rishnyak, I.: Choosing the method of finding similar images in the reverse search system. In: CEUR Workshop Proceedings, pp. 99–107 (2018)

    Google Scholar 

  50. Vysotska, V., Fernandes, V., Emmerich, M.: Web content support method in electronic business systems. In: CEUR Workshop Proceedings, vol. 2136, pp. 20–41 (2018)

    Google Scholar 

  51. Watkins, C., Dayan, P.: Q-learning. In: Machine Learning, vol. 8, pp. 279–292. Kluwer Academic Publishers, Boston (1992)

    Google Scholar 

  52. Weinberg, M., Rosenschein, J.: Best-response multiagent learning in non-stationary environments. In: AAMAS 2004, New York, USA (2004)

    Google Scholar 

  53. Weiss, G.: Multiagent Systems, 2nd edn. The MIT Press, Cambridge (2013)

    Google Scholar 

  54. Wooldridge, M.: An Introduction to Multiagent Systems. Wiley, Hoboken (2002)

    Google Scholar 

  55. Yang, S., Xu, J.X., Li, X., Shen, D.: Iterative Learning Control for Multi-Agent Systems Coordination. Wiley-IEEE Press (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Victoria Vysotska .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kravets, P., Lytvyn, V., Vysotska, V., Ryshkovets, Y., Vyshemyrska, S., Smailova, S. (2021). Dynamic Coordination of Strategies for Multi-agent Systems. In: Babichev, S., Lytvynenko, V., Wójcik, W., Vyshemyrskaya, S. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2020. Advances in Intelligent Systems and Computing, vol 1246. Springer, Cham. https://doi.org/10.1007/978-3-030-54215-3_42

Download citation

Publish with us

Policies and ethics