Predicting and Enhancing Hearthstone Strategy with Combinatorial Fusion

منو

" Predicting and Enhancing Hearthstone Strategy with Combinatorial Fusion "
Gorelick, Henry William Hsu, D. F.

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1054654
Doc. No	:	TL53771
Main Entry	:	Gorelick, Henry William
Title & Author	:	Predicting and Enhancing Hearthstone Strategy with Combinatorial Fusion\ Gorelick, Henry WilliamHsu, D. F.
College	:	Fordham University
Date	:	2020
Degree	:	M.S.
student score	:	2020
Note	:	62 p.
Abstract	:	The goal of this master’s thesis is to demonstrate that combinatorial fusion analysis (CFA) can effectively predict winners and enhance play strategy of Blizzard Entertainment’s collectible card game Hearthstone. CFA is used to combine and evaluate the performance of the combinatorial combinations of five machine learning models trained on 500 Hearthstone game simulations. For each combinatorial combination, the score function of the score combination and the score function of the rank combination is derived for each of the five models, and the performance of each is compared and evaluated. The improvement in performance of certain combinations over the individual components validates that CFA is an effective method for predicting the winner of Hearthstone games and enhancing play strategy. Furthermore, the resulting models could be used to boost Monte Carlo Tree Search and implement a competitive Hearthstone playing AI agent.
Descriptor	:	Artificial intelligence
	:	Arts management
	:	Computer engineering
	:	Computer science
	:	Design
Added Entry	:	Hsu, D. F.
Added Entry	:	Fordham University