Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study

• URL: http://arxiv.org/abs/2410.18605v1
• Date: Thu, 24 Oct 2024 09:59:10 GMT
• Title: Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study
• Authors: Tianze Wang, Maryam Honari-Jahromi, Styliani Katsarou, Olga Mikheeva, Theodoros Panagiotakopoulos, Oleg Smirnov, Lele Cao, Sahar Asadi,
• Abstract summary: This pilot study explores the application of language models (LMs) to model game event sequences. We transform raw event data into textual sequences and pretraining a Longformer model on this data. The results demonstrate the potential of self-supervised LMs in enhancing game design and personalization without relying on ground-truth labels.
• Score: 3.4333699338998693
• License:
• Abstract: This pilot study explores the application of language models (LMs) to model game event sequences, treating them as a customized natural language. We investigate a popular mobile game, transforming raw event data into textual sequences and pretraining a Longformer model on this data. Our 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.

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