Abstract

Fighting games represent a challenging problem for computer-controlled characters. Therefore, they have attracted considerable research interest. This paper investigates novel multi-objective neuroevolutionary approaches for fighting games focusing on the Fighting Game AI Competition. Considering several objectives shall improve the AI’s generalization capabilities when confronted with new opponents. To this end, novel combinations of neuroevolution and multi-objective evolutionary algorithms are explored. Since the variants proposed employ the well-known R2 indicator, we derived a novel faster algorithm for determining the exact R2 contribution. An experimental comparison of the novel variants to existing multi-objective neuroevolutionary algorithms demonstrates clear performance benefits on the test case considered. The best performing algorithm is then used to evolve controllers for the fighting game. Comparing the results with state-of-the-art AI opponents shows very promising results; the novel bot is able to outperform several competitors.

中文翻译：

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应对对手：战斗游戏的多目标进化神经网络

摘要

格斗游戏对于计算机控制的角色而言是一个具有挑战性的问题。因此，它们引起了相当大的研究兴趣。本文研究了针对格斗游戏AI竞赛的格斗游戏新颖的多目标神经进化方法。考虑到几个目标，当面对新的对手时，将提高AI的泛化能力。为此，探索了神经进化和多目标进化算法的新颖组合。由于建议的变体采用了众所周知的R 2指标，因此我们推导了一种新颖的更快算法来确定确切的R2贡献。对新型变量与现有多目标神经进化算法的实验比较表明，在考虑的测试用例上具有明显的性能优势。然后，使用性能最佳的算法来发展格斗游戏的控制器。将结果与最先进的AI对手进行比较显示出非常有希望的结果；这种新颖的机器人能够胜过多个竞争对手。