2018/10/08 by Felipe Zilio, Zilio, Felipe, Marcelo Prates +4 · 1 voice · 1 citation
Computer Science · Mathematics · #Artificial Intelligence in Games #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Music and Audio Processing #Video Analysis and Summarization #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1810.03744
10 pages, 1 figure, 9 tables. Accepted at ICONIP 2018
arxiv created 2018/10/08 · openalex publication_date 2018/10/08 · arxiv published 2018/10/08 · arxiv updated 2018/10/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.