In this work, we presented a study regarding two important aspects of evolving feature-based game evaluation functions: the choice of genome representation and the choice of opponent used to test the model. We compared three representations. One simpler and more limited, based on a vector of weights that are used in a linear combination of predefined game features. And two more complex, based on binary and n-ary trees. On top of this test, we also investigated the influence of fitness defined as a simulation-based function that: plays against a fixed weak opponent, plays against a fixed strong opponent, and plays against the best individual from the previous population. For a testbed, we have chosen a recently popular domain of digital collectible card games. We encoded our experiments in a programming game, Legends of Code and Magic, used in Strategy Card Game AI Competition. However, as the problems stated are of general nature we are convinced that our observations are applicable in the other domains as well.

中文翻译：

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收集式纸牌游戏AI不断发展的评估功能

在这项工作中，我们提出了一项有关基于特征的游戏评估功能演变的两个重要方面的研究：基因组表示的选择和用于测试模型的对手的选择。我们比较了三种表示形式。基于在预定义游戏功能的线性组合中使用的权重向量，这是一种更简单且更受限制的方式。还有两个更复杂的，基于二叉树和n元树。在此测试的基础上，我们还研究了适应度的影响，该适应度定义为基于模拟的函数，该函数：与固定的弱对手对抗，与固定的强对手对抗以及与先前群体中的最佳个人对抗。作为测试平台，我们选择了一个最近流行的数字可收藏纸牌游戏领域。我们在一个编程游戏《代码与魔术的传奇》中对实验进行了编码，在策略纸牌游戏AI竞赛中使用。但是，由于所述问题具有普遍性，因此我们深信我们的观察结果也适用于其他领域。