Name
Abstract | ||
|---|---|---|
In order to achieve flow and increase player retention, it is important that games difficulty matches player skills. Being able to evaluate how people play a game is a crucial component for detecting gamers strategies in video-games. One of the main problems in player strategy detection is whether attributes selected to define strategies correctly detect the actions of the player. In this paper, we will study a Real Time Strategy (RTS) game. In RTS the participants make use of units and structures to secure areas of a map and/or destroy the opponents resources. We will extract real-time information about the players strategies at several gameplays through a Web Platform. After gathering enough information, the model will be evaluated in terms of unsupervised learning (concretely, K-Means). Finally, we will study the similitude between several gameplays where players use different strategies. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10422-5_22 | Studies in Computational Intelligence |
Keywords | DocType | Volume |
Player Strategies,Video Games,Sliding Windows,K-Means,Real Time Strategy Game | Conference | 570 |
ISSN | Citations | PageRank |
1860-949X | 1 | 0.45 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fernando Palero | 1 | 10 | 1.43 |
| Cristian Ramírez-Atencia | 2 | 40 | 7.40 |
| David Camacho | 3 | 22 | 4.57 |