Neural Networks Models for Analyzing Magic: The Gathering Cards

Abstract

Historically, games of all kinds have often been the subject of study in scientific works of Computer Science, including the field of machine learning. By using machine learning techniques and applying them to a game with defined rules or a structured dataset, it's possible to learn and improve on the already existing techniques and methods to tackle new challenges and solve problems that are out of the ordinary. The already existing work on card games tends to focus on gameplay and card mechanics. This work aims to apply neural networks models, including Convolutional Neural Networks and Recurrent Neural Networks, in order to analyze Magic: the Gathering cards, both in terms of card text and illustrations; the card images and texts are used to train the networks in order to be able to classify them into multiple categories. The ultimate goal was to develop a methodology that could generate card text matching it to an input image, which was attained by relating the prediction values of the images and generated text across the different categories.

Toward a Competitive Agent Framework for Magic: The Gathering

“…Existing literature including (Fink, Pastel, and Sapra 2015;Pawlicki, Polin, and Zhang 2014) attempt to use machine learning techniques to predict the monetary value of a card based on its features: card type, keywords, mana cost, rarity, etc. A work by Zilio, et al (Zilio, Prates, and Lamb 2018) attempts to evaluate if a card properly predicts where it falls on the Magic "color pie" (Rosewater 2017). These ideas are in stark contrast with our evaluation of cards.…”