Play Style Identification Using Low-Level Representations of Play Traces in MicroRTS
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918091098685440 |
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| author | Xia, Ruizhe Yu Gow, Jeremy Lucas, Simon |
| author_facet | Xia, Ruizhe Yu Gow, Jeremy Lucas, Simon |
| contents | Play style identification can provide valuable game design insights and enable adaptive experiences, with the potential to improve game playing agents. Previous work relies on domain knowledge to construct play trace representations using handcrafted features. More recent approaches incorporate the sequential structure of play traces but still require some level of domain abstraction. In this study, we explore the use of unsupervised CNN-LSTM autoencoder models to obtain latent representations directly from low-level play trace data in MicroRTS. We demonstrate that this approach yields a meaningful separation of different game playing agents in the latent space, reducing reliance on domain expertise and its associated biases. This latent space is then used to guide the exploration of diverse play styles within studied AI players. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10172 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Play Style Identification Using Low-Level Representations of Play Traces in MicroRTS Xia, Ruizhe Yu Gow, Jeremy Lucas, Simon Machine Learning Artificial Intelligence Play style identification can provide valuable game design insights and enable adaptive experiences, with the potential to improve game playing agents. Previous work relies on domain knowledge to construct play trace representations using handcrafted features. More recent approaches incorporate the sequential structure of play traces but still require some level of domain abstraction. In this study, we explore the use of unsupervised CNN-LSTM autoencoder models to obtain latent representations directly from low-level play trace data in MicroRTS. We demonstrate that this approach yields a meaningful separation of different game playing agents in the latent space, reducing reliance on domain expertise and its associated biases. This latent space is then used to guide the exploration of diverse play styles within studied AI players. |
| title | Play Style Identification Using Low-Level Representations of Play Traces in MicroRTS |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.10172 |