Enhancing Dialogue Generation in Werewolf Game Through Situation Analysis and Persuasion Strategies (2024)

Abstract: Recent advancements in natural language processing, particularly with large language models (LLMs) like GPT-4, have significantly enhanced dialogue systems, enabling them to generate more natural and fluent conversations. Despite these improvements, challenges persist, such as managing continuous dialogues, memory retention, and minimizing hallucinations. The AIWolfDial2024 addresses these challenges by employing the Werewolf Game, an incomplete information game, to test the capabilities of LLMs in complex interactive environments. This paper introduces a LLM-based Werewolf Game AI, where each role is supported by situation analysis to aid response generation. Additionally, for the werewolf role, various persuasion strategies, including logical appeal, credibility appeal, and emotional appeal, are employed to effectively persuade other players to align with its actions.

## introduction in recent years, the rapid development of natural language processing (nlp) technology has brought dialogue systems, one of its core applications, into the spotlight of both academia and industry @xcite @xcite . the advent of large language models (llms) like gpt-4 (openai, 2023) has significantly improved the ability of dialogue systems to produce natural and fluent conversations. however, despite their impressive text generation capabilities, these models still encounter significant challenges. for instance, they struggle with managing continuous dialogue, retaining memory, and minimizing the generation of hallucinations (irrelevant or incorrect information) @xcite . these issues limit the effectiveness of dialogue systems in more complex interactive scenarios. in this context, the "aiwolfdial" international competition has emerged @xcite . this competition aims to explore and enhance the performance of llms in complex interactive environments by simulating the werewolf game, an incomplete information game. in such games, participants lack access to all critical information and must rely on reasoning, strategy, and communication to advance. the "aiwolfdial" places high demands on dialogue systems, requiring them to perform logical inference, persuasion, and even deception of other players, while also managing non-task-oriented dialogues in role-playing scenarios. this competition not only tests the systems' language generation capabilities but also evaluates their adaptability to complex interactions. this paper introduces the system architecture of our ai for various roles in the werewolf game, developed by the super_il team, where each role aids dialogue generation through game situation analysis. we have specifically enhanced the persuasion skills for the werewolf role, recognizing that persuasive techniques are crucial in the game, particularly for the werewolf, as it must influence other players' voting behavior to align with its own. in our system, the werewolf role achieves persuasion through multiple rounds of persuasive dialogue. specifically, we first employ a persuasion strategy based on logic and facts, presenting clear and compelling arguments to convince other players. next, we utilize a trust-based persuasion strategy to build trust and credibility with other players, thereby enhancing the effectiveness of persuasion. finally, we employ an emotion-driven persuasion strategy, using emotionally resonant language to deepen influence. this multi-dimensional persuasion strategy makes the werewolf role more convincing in the game. the contributions of this study are outlined below. ## werewolf game settings this study is set in the context of the werewolf game, as specified in aiwolfdial2024. each game involves five players: one seer, one werewolf, one possessed, and two villagers. the seer and villagers comprise the human team, while the possessed and werewolf make up the werewolf team. players are unaware of each other's roles. the game initiates on day 0 and continues until either the human team or the werewolf team is the sole survivor, with the game lasting no more than two days. day 0 involves only initial greetings among players. the seer's role activates on the night of day 0, allowing them to inspect one player's identity each night. all players, except for the werewolf, are identified as human. although the possessed belongs to the werewolf team, their identity will appear as "human" when inspected by the seer. from day 1 onwards, players engage in multiple dialogue rounds, with the order of speaking randomized in each round. after the daytime discussion phase ends, night falls. during the night, players first collectively vote to exile one player, followed by the werewolf attacking, and then the seer conducting their divination. if the werewolf is voted out on the first night, the game concludes immediately. ## the proposed system architecture this section details the specific system architecture of our llm-based werewolf game ai, with the key components depicted in figure 1 . the primary parts include the situation analysis module, the response generation module, the persuasive response generation module for the werewolf, and the voting module (not shown in the figure 1 ). ## case study of self-play figure 7 displays the complete game log from a self-play session using the system developed in this study, which incorporated the gpt-4o-2024-05-13 model during the situation analysis, response generation, and voting phases. analysis of the game log revealed that during the talk phase on day 1, all agents adeptly recognized the dynamic changes in the game situation through situation analysis and responded appropriately. the persuasive language employed by the werewolf role proved highly effective; by the end of the first night, all players except agent[01] had aligned their voting actions with the werewolf role. this outcome underscores the effectiveness of both the situation analysis module and the persuasive responses of the werewolf role. however, on day 2, the werewolf failed to persuade the remaining players, indicating that the llm demonstrated sufficient robustness to accurately recognize the current situation without being swayed by persuasive strategies. despite an adequate number of dialogue rounds being set, we observed that the discussions on day 1 were not as in-depth as expected, with agents repeating a lot of content. this issue might be attributed to the relatively simple setup of the five-player werewolf game and the fact that in our ai, roles other than the werewolf are based on similar methods, leading to a lack of diversity in performance during self-play. it also highlights the limitations of the llm in conducting more complex analyses and generating diverse responses. ultimately, even though the werewolf team theoretically could have secured an easy victory on day 2 with only one villager remaining, the game outcome did not reflect this. this underscores the inadequacies of our system in terms of adaptability and strategy execution for roles other than the werewolf, particularly the possessed. based on these observations, our goal is to further enhance the adaptability and decision-making abilities of other roles in future research. ## win rate against other teams in the formal competition of "aiwolfdial," ai agents from different teams were assigned specific roles to participate in the werewolf game. the competition featured two tracks: a japanese track 2 and an english track 3 , with the game logs publicly available. ## conclusion we present a llm-based werewolf game ai, developed by the super_il team, which participated in aiwolfdial2024. our proposed system architecture utilizes situation analysis to guide response generation and specifically enhances persuasive capabilities of the werewolf role through various persuasive strategies. the effectiveness of our architecture was validated through the analysis of game logs and formal competition win rates.