Name
Abstract | ||
|---|---|---|
In this paper, we demonstrate how to extract strategic knowledge from gaming data collected among players of the popular video game HearthStone. Our methodology is as follows. First we train a series of classifiers to predict the outcome of the game during a match, then we demonstrate how to spot key strategic events by tracking sudden changes in the classifier prediction. This methodology is applied to a large collection of HeathStone matches that we have collected from top ranked European players. Expert analysis shows that the events identified with this approach are both important and easy to interpret with the corresponding data. |
Year | Venue | Field |
|---|---|---|
2016 | MLSA@PKDD/ECML | Data mining,Ranking,Computer science,Artificial intelligence,Classifier (linguistics),Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boris Doux | 1 | 0 | 0.34 |
| Clément Gautrais | 2 | 1 | 3.44 |
| Benjamin Négrevergne | 3 | 35 | 5.44 |