Summary of Understanding Players As If They Are Talking to the Game in a Customized Language: a Pilot Study, by Tianze Wang et al.
Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study
by Tianze Wang, Maryam Honari-Jahromi, Styliani Katsarou, Olga Mikheeva, Theodoros Panagiotakopoulos, Oleg Smirnov, Lele Cao, Sahar Asadi
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of language models (LMs) to model game event sequences by treating them as customized natural language. Specifically, it investigates a popular mobile game, transforming raw event data into textual sequences and pretraining a Longformer model on this data. The approach captures the rich and nuanced interactions within game sessions, effectively identifying meaningful player segments. The results demonstrate the potential of self-supervised LMs in enhancing game design and personalization without relying on ground-truth labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how language models can be used to analyze events in a popular mobile game. It takes raw event data and turns it into text, then trains a special kind of model called Longformer on this data. The results are promising, as the model is able to identify different groups of players based on their behavior within the game. |
Keywords
» Artificial intelligence » Pretraining » Self supervised
