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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