Setting Players' Behaviors in World of Warcraft through Semi-Supervised Learning
This work addresses player modeling for digital games, specifically World of Warcraft, to enhance entertainment experiences, but it is incremental as it applies an existing method to new data.
The paper tackled the problem of modeling player behaviors in World of Warcraft to improve game interaction and player retention, using a semi-supervised learning technique on data from 2006-2009 to identify characteristics impacting behaviors like achiever and explorer.
Digital games are one of the major and most important fields on the entertainment domain, which also involves cinema and music. Numerous attempts have been done to improve the quality of the games including more realistic artistic production and computer science. Assessing the player's behavior, a task known as player modeling, is currently the need of the hour which leads to possible improvements in terms of: (i) better game interaction experience, (ii) better exploitation of the relationship between players, and (iii) increasing/maintaining the number of players interested in the game. In this paper we model players using the basic four behaviors proposed in \cite{BartleArtigo}, namely: achiever, explorer, socializer and killer. Our analysis is carried out using data obtained from the game "World of Warcraft" over 3 years (2006 $-$ 2009). We employ a semi-supervised learning technique in order to find out characteristics that possibly impact player's behavior.