Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking
Drafting, i.e., the iterative, adversarial selection of a subset of items from a larger candidate set, is a key element of many games and related problems. It encompasses team formation in sports or e-sports, as well as deck selection in formats of many modern card games. The key difficulty of drafting is that it is typically not sufficient to simply evaluate each item in a vacuum and to select the best items. The evaluation of an item depends on the context of the set of items that were already selected earlier, as the value of a set is not just the sum of the values of its members - it must include a notion of how well items go together. In this paper, we study drafting in the context of the card game Magic: The Gathering. We propose the use of the Contextual Preference Ranking framework, which learns to compare two possible extensions of a given deck of cards. We demonstrate that the resulting neural network is better able to better inform decisions in this game than previous attempts.