Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914987182653440 |
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| author | Wang, Tianze Honari-Jahromi, Maryam Katsarou, Styliani Mikheeva, Olga Panagiotakopoulos, Theodoros Smirnov, Oleg Cao, Lele Asadi, Sahar |
| author_facet | Wang, Tianze Honari-Jahromi, Maryam Katsarou, Styliani Mikheeva, Olga Panagiotakopoulos, Theodoros Smirnov, Oleg Cao, Lele Asadi, Sahar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18605 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study Wang, Tianze Honari-Jahromi, Maryam Katsarou, Styliani Mikheeva, Olga Panagiotakopoulos, Theodoros Smirnov, Oleg Cao, Lele Asadi, Sahar Machine Learning 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. |
| title | Understanding Players as if They Are Talking to the Game in a Customized Language: A Pilot Study |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.18605 |