Date of Award
Fall 2024
Project Type
Thesis
Program or Major
Computer Science
Degree Name
Master of Science
First Advisor
Wheeler Ruml
Second Advisor
Alexander Dockhorn
Third Advisor
Marek Petrik
Abstract
Duelyst II is an online collectible card game (CCG) that features a 9 × 5 grid board,making it a cross between the popular CCG Hearthstone and chess. It is a partially- observable stochastic game (POSG) with a large branching factor and the ability to take several actions in a time-limited turn, making it a challenging domain for game-playing AI. On top of that, successor generation is very slow, limiting the effectiveness of traditional planning approaches such as Monte Carlo tree search (MCTS).
In this thesis, I build on my previous work on Duelyst II AI, creating a stronger game-playing agent than the built-in rule-based one that is also practical to integrate into the live game. I accomplish this through enhanced planning, then beat that agent with one that uses action abstractions. I also investigate different methods of behavior cloning from game logs for enhancing abstraction-based planners. I find that learning leads to more intelligent selection of certain types of actions, but that those action types are not critical to an agent’s strength.
Recommended Citation
McKenney, Bryan, "Stronger Practical Game-Playing AI for Duelyst II" (2024). Master's Theses and Capstones. 1889.
https://scholars.unh.edu/thesis/1889