论文标题
基于零和游戏的Robocode机器人自适应对抗的分析
Analysis of Robocode Robot Adaptive Confrontation Based on Zero-Sum Game
论文作者
论文摘要
现代情报的对抗在某种程度上是一个不完整的信息对抗，在某种程度上，双方都没有访问足够的信息来检测对手的部署状态，然后智能有必要自适应地完成信息检索并在对抗环境中制定对抗策略。在本文中，包括七个坦克机器人，包括测试机器人，用于1V 1独立和混合对抗。本文的主要目的是验证Testrobot的零和游戏alpha-beta修剪算法的有效性，并结合对对手在游戏回合策略下的下一个时刻运动位置的估计以及提前释放智能机构自己的子弹以击中对手的效果。最后，根据对抗实验的结果，通过绘制1V1独立对抗的直方图和混合对抗的雷达图来表达坦克智能的自然性质差异。
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.
