Setting Players' Behaviors in World of Warcraft through Semi-Supervised Learning

TL;DR

This paper models player behaviors in World of Warcraft using semi-supervised learning to improve game interaction and player engagement by analyzing three years of game data.

Contribution

It introduces a semi-supervised learning approach to identify player behavior traits based on Bartle's four categories using extensive game data.

Findings

Identified key characteristics influencing player behaviors

Demonstrated effectiveness of semi-supervised learning in player modeling

Provided insights for enhancing game design and player retention

Abstract

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.