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arXiv · 2502.20807

Digital Player: Evaluating Large Language Models based Human-like Agent in Games

Jiawei Wang·Kai Wang·Shaojie Lin·Runze Wu·Bihan Xu·Lingeng Jiang·Shiwei Zhao·Renyu Zhu·Haoyu Liu·Zhipeng Hu·Zhong Fan·Le Li·Tangjie Lyu·Changjie Fan
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Abstract

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.

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Jiawei Wang, Kai Wang, Shaojie Lin, Runze Wu, Bihan Xu, Lingeng Jiang, Shiwei Zhao, Renyu Zhu, Haoyu Liu, Zhipeng Hu, Zhong Fan, Le Li, Tangjie Lyu, Changjie Fan. 2025-02-28. Digital Player: Evaluating Large Language Models based Human-like Agent in Games. https://arxiv.org/abs/2502.20807

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