Digital Player: Evaluating Large Language Models based Human-like Agent in Games
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912251741470720 |
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| author | Wang, Jiawei Wang, Kai Lin, Shaojie Wu, Runze Xu, Bihan Jiang, Lingeng Zhao, Shiwei Zhu, Renyu Liu, Haoyu Hu, Zhipeng Fan, Zhong Li, Le Lyu, Tangjie Fan, Changjie |
| author_facet | Wang, Jiawei Wang, Kai Lin, Shaojie Wu, Runze Xu, Bihan Jiang, Lingeng Zhao, Shiwei Zhu, Renyu Liu, Haoyu Hu, Zhipeng Fan, Zhong Li, Le Lyu, Tangjie Fan, Changjie |
| contents | With the rapid advancement of Large Language Models (LLMs), LLM-based autonomous agents have shown the potential to function as digital employees, such as digital analysts, teachers, and programmers. In this paper, we develop an application-level testbed based on the open-source strategy game "Unciv", which has millions of active players, to enable researchers to build a "data flywheel" for studying human-like agents in the "digital players" task. This "Civilization"-like game features expansive decision-making spaces along with rich linguistic interactions such as diplomatic negotiations and acts of deception, posing significant challenges for LLM-based agents in terms of numerical reasoning and long-term planning. Another challenge for "digital players" is to generate human-like responses for social interaction, collaboration, and negotiation with human players. The open-source project can be found at https:/github.com/fuxiAIlab/CivAgent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20807 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Digital Player: Evaluating Large Language Models based Human-like Agent in Games Wang, Jiawei Wang, Kai Lin, Shaojie Wu, Runze Xu, Bihan Jiang, Lingeng Zhao, Shiwei Zhu, Renyu Liu, Haoyu Hu, Zhipeng Fan, Zhong Li, Le Lyu, Tangjie Fan, Changjie Machine Learning With the rapid advancement of Large Language Models (LLMs), LLM-based autonomous agents have shown the potential to function as digital employees, such as digital analysts, teachers, and programmers. In this paper, we develop an application-level testbed based on the open-source strategy game "Unciv", which has millions of active players, to enable researchers to build a "data flywheel" for studying human-like agents in the "digital players" task. This "Civilization"-like game features expansive decision-making spaces along with rich linguistic interactions such as diplomatic negotiations and acts of deception, posing significant challenges for LLM-based agents in terms of numerical reasoning and long-term planning. Another challenge for "digital players" is to generate human-like responses for social interaction, collaboration, and negotiation with human players. The open-source project can be found at https:/github.com/fuxiAIlab/CivAgent. |
| title | Digital Player: Evaluating Large Language Models based Human-like Agent in Games |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.20807 |