让狼人角色根据玩家态度动态换策略，提升游戏表现。
Strategy Adaptation in Large Language Model Werewolf Agents
- 基于对话上下文和玩家角色推测，显式切换策略
- 相比固定或隐式策略，胜率显著提升
- 适合需要动态决策的多人博弈场景
本研究提出一种方法，通过根据其他玩家的态度和对话上下文，动态切换预设策略来提升狼人角色的表现。以往基于提示工程的狼人代理采用隐式定义的有效策略，无法适应局势变化。本文方法则显式依据游戏情境和对其他玩家角色的估计，选择合适策略。我们对比了采用策略自适应的狼人代理与使用隐式或固定策略的基线代理，验证了所提方法的有效性。
原文摘要 · Abstract (English)
This study proposes a method to improve the performance of Werewolf agents by switching between predefined strategies based on the attitudes of other players and the context of conversations. While prior works of Werewolf agents using prompt engineering have employed methods where effective strategies are implicitly defined, they cannot adapt to changing situations. In this research, we propose a method that explicitly selects an appropriate strategy based on the game context and the estimated roles of other players. We compare the strategy adaptation Werewolf agents with baseline agents using implicit or fixed strategies and verify the effectiveness of our proposed method.
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