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        <title>An Implementation of Werewolf Agent That does not Truly Trust LLMs</title>
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    <name type="personal">
        <namePart type="given">Takehiro</namePart>
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        <namePart type="given">Shintaro</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2nd International AIWolfDial Workshop</title>
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            <namePart type="given">Yoshinobu</namePart>
            <namePart type="family">Kano</namePart>
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    <abstract>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.</abstract>
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        <url>https://aclanthology.org/2024.aiwolfdial-1.7</url>
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    <part>
        <date>2024-09</date>
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            <start>58</start>
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%0 Conference Proceedings
%T An Implementation of Werewolf Agent That does not Truly Trust LLMs
%A Sato, Takehiro
%A Ozaki, Shintaro
%A Yokoyama, Daisaku
%Y Kano, Yoshinobu
%S Proceedings of the 2nd International AIWolfDial Workshop
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F sato-etal-2024-implementation
%X 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.
%U https://aclanthology.org/2024.aiwolfdial-1.7
%P 58-67

Markdown (Informal)

[An Implementation of Werewolf Agent That does not Truly Trust LLMs](https://aclanthology.org/2024.aiwolfdial-1.7) (Sato et al., AIWolfDial-WS 2024)

• An Implementation of Werewolf Agent That does not Truly Trust LLMs (Sato et al., AIWolfDial-WS 2024)

ACL

• Takehiro Sato, Shintaro Ozaki, and Daisaku Yokoyama. 2024. An Implementation of Werewolf Agent That does not Truly Trust LLMs. In Proceedings of the 2nd International AIWolfDial Workshop, pages 58–67, Tokyo, Japan. Association for Computational Linguistics.