Player Behavior Modeling for Enhancing Role-Playing Game Engagement
Bibliographic Data
| ID | 22108191 |
|---|---|
| Authors | Sha Zhao (0000-0003-4628-5198, Zhejiang University), Yizhi Xu (0009-0007-1692-4688, Zhejiang University), Zhiling Luo (0000-0002-0540-7307, Zhejiang University), Jianrong Tao (0000-0003-1807-9522, NetEase (China)), Shijian Li (0000-0001-5846-3065, Zhejiang University), Changjie Fan (0000-0001-5420-0516, NetEase (China)), Gang Pan (0000-0003-0920-3018, Zhejiang University) |
| Year | 2021 |
| Volume | 8 |
| Issue | 2 |
| Pages | 464-474 |
| Publication date | 2021-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2021.3052261 |
| OpenAlex | W3126813021 |
| Language | EN |
| References cited | 20 |
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
Game art design · Game design · Game design document · Game Developer · Game mechanics · Game theory · Human–computer interaction · Metagaming · Multimedia · Narrative · Non-cooperative game · Screening game · Simultaneous game · Video game · Video game design · Artificial Intelligence in Games · Computer Science · Digital Games and Media · Educational Games and Gamification · Mathematics
| Highly cited | No |
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