Analysis of Robocode Robot Adaptive Confrontation Based on Zero-Sum Game

TL;DR

This paper investigates the effectiveness of a zero-sum game strategy combined with predictive algorithms in robotic confrontations, demonstrating improved combat performance in simulated tank battles.

Contribution

It introduces a novel application of zero-sum game algorithms with opponent motion estimation in robotic confrontation strategies.

Findings

TestRobot's strategy outperforms baseline methods in simulated battles.

Predictive opponent motion estimation enhances attack accuracy.

Histograms and radar plots illustrate differences in robot performance.

Abstract

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.