Digital collectible card games are not only a growing part of the video game industry, but also an interesting research area for the field of computational intelligence. This game genre allows researchers to deal with hidden information, uncertainty and planning, among other aspects. This paper proposes the use of evolutionary algorithms (EAs) to develop agents who play a card game, Hearthstone, by optimizing a data-driven decision-making mechanism that takes into account all the elements currently in play. Agents feature self-learning by means of a competitive coevolutionary training approach, whereby no external sparring element defined by the user is required for the optimization process. One of the agents developed through the proposed approach was runner-up (best 6%) in an international Hearthstone Artificial Intelligence (AI) competition. Our proposal performed remarkably well, even when it faced state-of-the-art techniques that attempted to take into account future game states, such as Monte-Carlo Tree search. This outcome shows how evolutionary computation could represent a considerable advantage in developing AIs for collectible card games such as Hearthstone.

翻译：数字集换式卡牌游戏不仅是电子游戏产业中日益增长的重要组成部分，也为计算智能领域提供了有趣的研究方向。此类游戏使研究者能够处理隐藏信息、不确定性与规划等多方面问题。本文提出使用进化算法（EAs）开发《炉石传说》卡牌游戏智能体，通过优化数据驱动的决策机制来综合考虑当前对局中的所有元素。智能体采用竞争性协同进化训练方法实现自我学习，该优化过程无需用户定义外部陪练元素。通过所提方法开发的智能体曾在一项国际《炉石传说》人工智能（AI）竞赛中获得亚军（前6%）。即使面对蒙特卡洛树搜索等尝试考虑未来游戏状态的先进技术，我们的方案仍表现优异。这一结果表明，进化计算可为《炉石传说》等集换式卡牌游戏的AI开发带来显著优势。