Deep Learning Techniques for Explainable Resource Scales in Collectible Card Games

In collectible card games, developers face the challenge of creating new, and interesting cards that are not too strong or game-breaking, retaining the game’s overall balance. Over time, this becomes challenging due to the sheer volume of the published content. In this article, we propose a framework for generating models capable of recommending resource scales, a pivotal point in balancing. We evaluate the usage of several state-of-the-art neural architectures to learn representations for text followed by gradient boosting decision trees to incorporate remaining features. Throughout our analysis, we present various explanation tools that should empower game developers, and aid them with new insights. In particular, we present the sets of words that drive the model in diverse situations, such as when it was inaccurate by a small margin. We also exhibit instances where textual features cannot give an accurate prediction, requiring additional information. Our method achieves a mean reciprocal rank of
$+0.8$
when evaluated on popular card games, even though superficially identical cards might have distinct costs.