一种不真正信任大语言模型的狼人游戏智能体的实现
An Implementation of Werewolf Agent That does not Truly Trust LLMs
AI总结：
本文提出一种结合大语言模型与规则算法的狼人游戏智能体，通过分析对话历史选择LLM或模板输出，以缓解对话不一致并实现情境反驳、适时结束对话和人设行为，定性评估显示其比纯LLM更像人类。
摘要：
狼人游戏是一种不完全信息博弈，在创建计算机智能体作为玩家时面临若干挑战，原因在于缺乏对局势的理解以及话语的个性化（例如，计算机智能体无法进行富有个性的话语或情境性说谎）。我们提出了一种狼人游戏智能体，通过结合大语言模型（LLM）与基于规则的算法来解决其中部分困难。具体而言，我们的智能体使用基于规则的算法，根据利用大语言模型分析对话历史的结果，从大语言模型或预先准备的模板中选择输出。这使得智能体能够在特定情境下进行反驳，识别何时结束对话，并以特定人设行事。该方法缓解了对话不一致的问题，并因此促进了合乎逻辑的话语表达。我们还进行了定性评估，结果表明与未经修改的大语言模型相比，我们的智能体被认为更像人类。该智能体可自由获取，以促进狼人游戏领域的研究进展。
英文摘要：
Werewolf is an incomplete information game, which has several challenges when creating a computer agent as a player given the lack of understanding of the situation and individuality of utterance (e.g., computer agents are not capable of characterful utterance or situational lying). We propose a werewolf agent that solves some of those difficulties by combining a Large Language Model (LLM) and a rule-based algorithm. In particular, our agent uses a rule-based algorithm to select an output either from an LLM or a template prepared beforehand based on the results of analyzing conversation history using an LLM. It allows the agent to refute in specific situations, identify when to end the conversation, and behave with persona. This approach mitigated conversational inconsistencies and facilitated logical utterance as a result. We also conducted a qualitative evaluation, which resulted in our agent being perceived as more human-like compared to an unmodified LLM. The agent is freely available for contributing to advance the research in the field of Werewolf game.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
