Robot Coalition Formation Based on Fuzzy Cooperative Games over Blockchain-Based Smart Contracts

  • Alexander Smirnov
  • Leonid Sheremetov
  • Nikolay TeslyaEmail author
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 565)


In production cyber physical systems robots perform most operations. On the way to Industry 4.0 robots have to be automated and perform operation in coalition to reach common goals. The paper describes an approach to dynamic formation of coalitions of autonomous robots based on the integration of fuzzy cooperative games and smart contracts. Each robot is viewed as an agent, negotiating and bidding with others during the coalition forming for distribution of joint winnings. It is necessary to find combination of robots in way to maximize efficiency of joint work, while the efficiency of the entire coalition is unknown beforehand. A cooperative game with fuzzy core is used to form a coalition of robots allowing coordinating the actions of individual members to achieve a common goal, as well as to evaluate and distribute the overall benefit. To implement the negotiation process and record the composition of the coalition and the responsibilities of individual participants, it is proposed to use the smart contract technology, which now become a part of the blockchain technology. Smart contracts are proposed to be used as entity holding requirements and expected winnings of each participant in the immutable structure of a blockchain network. The final agreement can also be stored by all participants in form of smart contract that contains the distribution coefficients of the winnings given all the conditions of participation in the coalition. The availability of smart contracts to all participants in the coalition makes it possible to ensure joint control over the fulfillment of the task assigned to the coalition.


Fuzzy logic Coalition Coalition game Smart contract Robot 



The present research was supported by the projects funded through grants number 17-29-07073, 17-07-00247 and 17-07-00327 of the Russian Foundation for Basic Research.


  1. 1.
    Bayram, H., Bozma, H.I.: Coalition formation games for dynamic multirobot tasks. Int. J. Robot. Res. 35, 514–527 (2015). Scholar
  2. 2.
    Kashevnik, A., Teslya, N.: Blockchain-oriented coalition formation by CPS resources: ontological approach and case study. Electronics 7, 16 (2018). Scholar
  3. 3.
    Mouradian, C., Sahoo, J., Glitho, R.H., Morrow, M.J., Polakos, P.A.: A coalition formation algorithm for Multi-Robot Task Allocation in large-scale natural disasters. In: 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pp 1909–1914. IEEE (2017)Google Scholar
  4. 4.
    Guerrero, J., Oliver, G.: Multi-robot coalition formation in real-time scenarios. Robot. Auton. Syst. 60, 1295–1307 (2012). Scholar
  5. 5.
    Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Wooldridge, M., Sierra, C.: Automated negotiation: prospects, methods and challenges. Group Decis. Negot. 10, 199–215 (2001). Scholar
  6. 6.
    Smirnov, A., Teslya, N.: Robot interaction through smart contract for blockchain-based coalition formation. In: Chiabert, P., Bouras, A., Noël, F., Ríos, J. (eds.) PLM 2018. IAICT, vol. 540, pp. 611–620. Springer, Cham (2018). Scholar
  7. 7.
    Hosam, H., Khaldoun, Z.: Planning coalition formation under uncertainty: auction approach. In: Proceedings - 2006 International Conference on Information and Communication Technologies: From Theory to Applications, ICTTA 2006, pp. 3013–3017. IEEE (2006)Google Scholar
  8. 8.
    Aubin, J.-P.: Cooperative fuzzy games. Math. Oper. Res. 6, 1–13 (1981). Scholar
  9. 9.
    Kahan, J.P., Rapoport, A.: Theories of Coalition Formation. Lawrence Erlbaum Associates Inc, Routledge (1984)zbMATHGoogle Scholar
  10. 10.
    Gillies, D.B.: Some theorems on n-person games. Princeton University (1953)Google Scholar
  11. 11.
    Klusch, M., Gerber, A.: Dynamic coalition formation among rational agents. IEEE Intell. Syst. 17, 42–47 (2002). Scholar
  12. 12.
    Mareš, M.: Fuzzy Cooperative Games. Physica-Verlag HD, Heidelberg (2001)CrossRefGoogle Scholar
  13. 13.
    Shen, P., Gao, J.: Coalitional game with fuzzy payoffs and credibilistic core. Soft. Comput. 15, 781–786 (2010). Scholar
  14. 14.
    Smirnov, A.V., Sheremetov, L.B.: Models of coalition formation among cooperative agents: the current state and prospects of research. Sci. Tech. Inf. Process. 39, 283–292 (2012). Scholar
  15. 15.
    Verma, D., Desai, N., Preece, A., Taylor, I.: A block chain based architecture for asset management in coalition operations. In: Pham, T., Kolodny, M.A. (eds.) Proceedings of the SPIE 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, p. 101900Y (2017)Google Scholar
  16. 16.
    Dorri, A., Kanhere, S.S., Jurdak, R.: Towards an optimized BLOCKCHAIN for IoT. In: Proceedings of the Second International Conference on Internet-of-Things Design and Implementation - IoTDI 2017, pp. 173–178 (2017).
  17. 17.
    Zhang, Y., Wen, J.: The IoT electric business model: using blockchain technology for the internet of things. Peer-to-Peer Netw. Appl. 10, 983–994 (2017). Scholar
  18. 18.
    Castelló Ferrer, E.: The blockchain: a new framework for robotic swarm systems. In: Arai, K., Bhatia, R., Kapoor, S. (eds.) FTC 2018. AISC, vol. 881, pp. 1037–1058. Springer, Cham (2019). Scholar
  19. 19.
    Cachin, C., Vukolić, M.: Blockchain consensus protocols in the wild 24 (2017).
  20. 20.
    Sheremetov, L.B., Smirnov, A.V.: A fuzzy cooperative game model for configuration management for open supply networks. Contrib. Game Theory Manag. 4, 433–446 (2011)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Sheremetov, L.B.: A model of fuzzy coalition games in problems of configuring open supply networks. J. Comput. Syst. Sci. Int. 48, 765–778 (2009). Scholar
  22. 22.
    Zadeh, L.A.: Similarity relations and fuzzy orderings. Inf. Sci. 3, 177–200 (1971). Scholar
  23. 23.
    Haekwan, L., Tanaka, H.: Fuzzy approximations with non-symmetric fuzzy parameters in fuzzy regression analysis. J. Oper. Res. Soc. Japan 42, 98–112 (1999)MathSciNetzbMATHGoogle Scholar
  24. 24.
    Androulaki, E., Manevich, Y., Muralidharan, S., Murthy, C., Nguyen, B., Sethi, M., Hyperledger fabric. In: Proceedings of the Thirteenth EuroSys Conference on - EuroSys 2018, pp 1–15. ACM Press, New York (2018)Google Scholar

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© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.SPIIRASSt. PetersburgRussia
  2. 2.Mexican Petroleum Institute, Eje Central Lázaro Cárdenas NorteMexico CityMexico

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