系统梳理大模型社交代理在博弈场景中的研究进展
A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios
- 按博弈框架、代理能力、评估协议三部分归纳研究
- 覆盖从选择到沟通的多种博弈场景，分析代理决策机制
- 适合关注智能体社会性与博弈能力的研究者参考
博弈论场景已成为评估基于大语言模型（LLM）社交代理社会智能的关键范式。尽管已有大量研究探索此类代理在博弈环境中的表现，但尚缺乏全面系统的综述。为此，本文系统回顾了当前关于LLM驱动社交代理在博弈场景中的研究成果，将其归纳为三大核心部分：博弈框架、社交代理与评估协议。博弈框架涵盖从侧重选择到侧重沟通的多样化游戏场景；社交代理部分探讨代理的偏好、信念、推理能力及其交互对决策的影响；评估协议则包含通用与特定于游戏的评价指标。此外，本文分析了现有社交代理在不同博弈场景下的表现，并反思当前研究局限，提出未来发展方向，为推动博弈场景中社交代理的开发与评估提供洞见。
原文摘要 · Abstract (English)
Game-theoretic scenarios have become pivotal in evaluating the social intelligence of Large Language Model (LLM)-based social agents. While numerous studies have explored these agents in such settings, there is a lack of a comprehensive survey summarizing the current progress. To address this gap, we systematically review existing research on LLM-based social agents within game-theoretic scenarios. Our survey organizes the findings into three core components: Game Framework, Social Agent, and Evaluation Protocol. The game framework encompasses diverse game scenarios, ranging from choice-focusing to communication-focusing games. The social agent part explores agents' preferences, beliefs, and reasoning abilities, as well as their interactions and synergistic effects on decision-making. The evaluation protocol covers both game-agnostic and game-specific metrics for assessing agent performance. Additionally, we analyze the performance of current social agents across various game scenarios. By reflecting on the current research and identifying future research directions, this survey provides insights to advance the development and evaluation of social agents in game-theoretic scenarios.
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