Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking

• URL: http://arxiv.org/abs/2105.11864v1
• Date: Tue, 25 May 2021 12:07:27 GMT
• Title: Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking
• Abstract summary: We study drafting in the context of the card game Magic: The Gathering. We propose the use of a contextual preference network, which learns to compare two possible extensions of a given deck of cards. We demonstrate that the resulting network is better able to evaluate card decks in this game than previous attempts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Drafting, i.e., the 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 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 a contextual preference network, which learns to compare two possible extensions of a given deck of cards. We demonstrate that the resulting network is better able to evaluate card decks in this game than previous attempts.

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