4 research outputs found
Looking for Archetypes: Applying Game Data Mining to Hearthstone Decks
Digital Collectible Cards Games such as Hearthstone have become a very
proli c test-bed for Arti cial Intelligence algorithms. The main researches
have focused on the implementation of autonomous agents (bots) able to effectively
play the game. However, this environment is also very attractive for
the use of Data Mining (DM) and Machine Learning (ML) techniques, for
analysing and extracting useful knowledge from game data. The objective
of this work is to apply existing Game Mining techniques in order to study
more than 600,000 real decks (groups of cards) created by players with many
di erent skill levels. Data visualisation and analysis tools have been applied,
namely, Graph representations and Clustering techniques. Then, an expert
player has conducted a deep analysis of the results yielded by these methods,
aiming to identify the use of standard - and well-known - archetypes de ned
by the players. The used methods will also make it possible for the expert to
discover hidden relationships between cards that could lead to nding better
combinations of them, enhancing players' decks or, otherwise, identify unbalanced
cards that could lead to a disappointing game experience. Moreover,
although this work is mostly focused on data analysis and visualization, the
obtained results can be applied to improve Hearthstone Bots' behaviour, e.g.
predicting opponent's actions after identifying a speci c archetype in his/her
deck.Spanish Government PID2020-113462RB-I00
PID2020-115570 GB-C22Junta de Andalucia B-TIC-402-UGR18
P18-RT-4830
A-TIC-608-UGR2
Counterfactual Regret Minimization を用いたトレーディングカードゲームの戦略計算
学位の種別: 修士University of Tokyo(東京大学