Publication Date
Spring 2026
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Robert Chun
Second Advisor
Mark Stamp
Third Advisor
William Andreopoulos
Keywords
machine learning, decision-making, game theory, combinatorial optimization, learning-to-rank, supervised learning
Abstract
Artificial intelligence has demonstrated strong performance in complex decision-making domains such as chess and Go, motivating research into its application for games with even richer rules and combinatorial complexity. In collectible card games like Magic: The Gathering, deck construction from a constrained card pool is a critical and challenging task that requires evaluating card strength, synergy, and resource balance. This project explores whether machine learning, specifically learning-to-rank (LTR), can effectively model these human decision processes to construct competitive decks in a sealed format. The results found here can also be applicable to other areas of note, such as sports drafting with given data of players and their previous game statistics or corporate team formation given their resumes along with their appropriate skill sets.
Recommended Citation
Nguyen, Michael Dinh, "Modeling Sealed Deck Construction in Collectible Card Games Using Learning-to-Rank Approach" (2026). Master's Projects. 1811.
DOI: https://doi.org/10.31979/etd.c2cx-v8d4
https://scholarworks.sjsu.edu/etd_projects/1811
