The confrontation of modern intelligence is to some extent a non-complete information confrontation, where neither side has access to sufficient information to detect the deployment status of the adversary, and then it is necessary for the intelligence to complete information retrieval adaptively and develop confrontation strategies in the confrontation environment. In this paper, seven tank robots, including TestRobot, are organized for 1V 1 independent and mixed confrontations. The main objective of this paper is to verify the effectiveness of TestRobot's Zero-sum Game Alpha-Beta pruning algorithm combined with the estimation of the opponent's next moment motion position under the game round strategy and the effect of releasing the intelligent body's own bullets in advance to hit the opponent. Finally, based on the results of the confrontation experiments, the natural property differences of the tank intelligence are expressed by plotting histograms of 1V1 independent confrontations and radar plots of mixed confrontations.

本文通过组织七个坦克机器人进行独立与混合对决，并结合博弈策略下的α-β剪枝算法和对手下一步运动位置的估计以及提前释放自身子弹命中对手的效果，验证了TestRobot的零和博弈Alpha-Beta剪枝算法的有效性。文章通过绘制1V1独立对决的直方图和混合对决的雷达图表达了坦克智能的自然属性差异。

基于零和博弈的Robocode机器人自适应对抗分析