Predicting and Enhancing Hearthstone Strategy with Combinatorial Fusion
نام عام مواد
[Thesis]
نام نخستين پديدآور
Gorelick, Henry William
نام ساير پديدآوران
Hsu, D. F.
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
Fordham University
تاریخ نشرو بخش و غیره
2020
يادداشت کلی
متن يادداشت
62 p.
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
M.S.
کسي که مدرک را اعطا کرده
Fordham University
امتياز متن
2020
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
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.
اصطلاحهای موضوعی کنترل نشده
اصطلاح موضوعی
Artificial intelligence
اصطلاح موضوعی
Arts management
اصطلاح موضوعی
Computer engineering
اصطلاح موضوعی
Computer science
اصطلاح موضوعی
Design
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )