A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 2 Issue 1
Jan.  2015

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Wenzhong Zha, Jie Chen and Zhihong Peng, "Dynamic Multi-team Antagonistic Games Model with Incomplete Information and Its Application to Multi-UAV," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 1, pp. 74-84, 2015.
Citation: Wenzhong Zha, Jie Chen and Zhihong Peng, "Dynamic Multi-team Antagonistic Games Model with Incomplete Information and Its Application to Multi-UAV," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 1, pp. 74-84, 2015.

Dynamic Multi-team Antagonistic Games Model with Incomplete Information and Its Application to Multi-UAV


This work was supported by Foundation for Innovative Research Groups of National Natural Science Foundation of China (NSFC)(61321002), National Science Fund for Distinguished Young Scholars (60925011), Projects of Major International (Regional) Joint Research Program NSFC (61120106010), Beijing Education Committee Cooperation Building Foundation Project, Program for Changjiang Scholars and Innovative Research Team in University (IRT1208), Chang Jiang Scholars Program and National Natural Science Foundation of China (61203078).

  • At present, the studies on multi-team antagonistic games (MTAGs) are still in the early stage, because this complicated problem involves not only incompleteness of information and conflict of interests, but also selection of antagonistic targets. Therefore, based on the previous researches, a new framework is proposed in this paper, which is dynamic multi-team antagonistic games with incomplete information (DMTAGII) model. For this model, the corresponding concept of perfect Bayesian Nash equilibrium (PBNE) is established and the existence of PBNE is also proved. Besides, an interactive iteration algorithm is introduced according to the idea of the best response for solving the equilibrium. Then, the scenario of multiple unmanned aerial vehicles (UAVs) against multiple military targets is studied to solve the problems of tactical decision making based on the DMTAGII model. In the process of modeling, the specific expressions of strategy, status and payoff functions of the games are considered, and the strategy is coded to match the structure of genetic algorithm so that the PBNE can be solved by combining the genetic algorithm and the interactive iteration algorithm. Finally, through the simulation the feasibility and effectiveness of the DMTAGII model are verified. Meanwhile, the calculated equilibrium strategies are also found to be realistic, which can provide certain references for improving the autonomous ability of UAV systems.


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  • [1]
    Rasmussen S J, Shima T. UAV Cooperative Decision and Control:Challenges and Practical Approaches. Society for Industrial and Applied Mathematics, 2009. 15-19
    Bardhan R, Ghose D. Resource allocation and coalition formation for UAVs:a cooperative game approach. In:Proceedings of the 22nd International Conference on Control Applications. Hyderabad, India:IEEE, 2013. 1200-1205
    Semsar-Kazerooni E, Khorasani K. Multi-agent team cooperation:a game theory approach. Automatica, 2009, 45(10):2205-2213
    Sandholm W H. Population Games and Evolutionary Dynamics. Massachusetts:MIT Press, 2011. 1-15
    von Stengel B, Koller D. Team-maxmin equilibria. Games and Economic Behavior, 1997, 21(1-2):309-321
    Liu Y, Simaan M A. Noninferior Nash strategies for multi-team systems. Journal of Optimization Theory and Applications, 2004, 12(1):29-51
    Ahmed E, Hegazi A S, Elettreby M F, Asker S S. On multi-team games. Physica A, 2006, 369(2):809-816
    Elettreby M F, Hassan S Z. Dynamical multi-team Cournot game. Chaos, Solitons and Fractals, 2006, 27(3):666-672
    Asker S S. On dynamical multi-team Cournot game in exploitation of a renewable resource. Chaos, Solitons and Fractals, 2007, 32(1):264-268
    Harsanyi J C. Game with incomplete information played by bayesian players part III:the basic probability distribution of the game. Management Science, 1968, 14(7):486-502
    Chen J, Zha W Z, Peng Z H, Zhang J. Cooperative area reconnaissance for multi-UAV in dynamic environment. In:Proceedings of the 9th Asian Control Conference. Istanbul, Turkey:IEEE, 2013. 1-6
    Zhao Ming, Su Xiao-Hong, Ma Pei-Jun, Zhao Ling-Ling. A unified modeling method of UAVs cooperative target assignment by complex multi-constraint conditions. Acta Automatica Sinica, 2012, 38(12):2038-2048(in Chinese)
    Hui Yi-Nan, Zhu Hua-Yong, Shen Lin-Cheng. Study on dynamic game method with incomplete information in UAV attack-defends campaign. Ordnance Industry Automation, 2009, 28(1):4-7(in Chinese)
    Chen Xia, Liu Min, Hu Yong-Xin. Study on UAV offensive defensive game strategy based on uncertain information. Acta Armamentarii, 2012, 33(12):1510-1514(in Chinese)
    Emre K, Gokhan I. Exploiting delayed and imperfect information for generating approximate UAV target interception strategy. Journal of Intelligent and Robotic Systems, 2013, 69(1-4):313-329
    Bhattacharya S, Basar T. Differential game-theoretic approach to a spatial jamming problem. Advances in Dynamic Games, 2013, 12:245-268
    Herbert Gintis. Game Theory Evolving:A Problem-Centered Introduction to Modeling Strategic Interaction. New Jersey:Princeton University Press, 2008. 41-45
    Mei S W, Zhu J Q. Mathematical and control scientific issues of smart grid and its prospects. Acta Automatica Sinica, 2013, 39(2):119-131


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