Paper Info
Title
Player Behavior Modeling for Enhancing Role-Playing Game Engagement
Abstract
Role-playing games (RPGs) are one of the most exciting and most rapidly expanding genres of online games. Virtual characters that are not controlled by players, have become an integral part, which helps to advance narratives of RPGs. Believable characters can enhance game engagement and further improve player retention. However, game players easily find that most characters' behaviors are limited and improbable, resulting in a less meaningful game experience. In this work, we propose a framework to model game behaviors to learn behavior patterns of human players. Based on the learned behavior patterns, it generates human-like action sequences that can be used for the design of believable virtual characters in RPGs, so as to enhance game engagement. Specifically, considering the influence of game context in behavior patterns, we integrate game context (players' levels and game classes) with actions together to model behaviors. We propose a long-term memory cell on actions and game context to learn the hidden representations. We also introduce an attention mechanism to measure the contribution of the actions previously performed to the next action. Given only one action, our model can generate action sequences by predicting the succeeding action based on the previously generated actions. The model was evaluated on a real-world data set of over 22 000 players and more than 51 million action logs of an RPG game in 21 days. The results demonstrate the state-of-the-art performance.
Year
DOI
Venue
2021
10.1109/TCSS.2021.3052261
IEEE Transactions on Computational Social Systems
Keywords
DocType
Volume
Behavioral sequence generation,player behavior modeling,role-playing games (RPGs)
Journal
8
Issue
ISSN
Citations 
2
2329-924X
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Sha Zhao1489.96
Yizhi Xu200.34
Zhiling Luo3388.77
Jianrong Tao45111.96
Shijian Li5115569.34
Changjie Fan65721.37
Gang Pan71501123.57