通过情境分析与说服策略增强狼人游戏中的对话生成
Enhancing Dialogue Generation in Werewolf Game Through Situation Analysis and Persuasion Strategies
AI总结：
本文提出基于大语言模型的狼人游戏AI，通过情境分析辅助各角色对话生成，并为狼人角色引入逻辑、可信度和情感诉求等说服策略，以提升在不完全信息博弈中的交互与说服能力。
摘要：
自然语言处理的最新进展，尤其是GPT-4等大语言模型（LLMs），显著增强了对话系统，使其能够生成更自然流畅的对话。尽管有这些改进，挑战依然存在，例如管理连续对话、记忆保持以及最小化幻觉。AIWolfDial2024通过采用狼人游戏这一不完全信息博弈，在复杂交互环境中测试LLMs的能力来应对这些挑战。本文介绍了一个基于LLM的狼人游戏AI，其中每个角色都由情境分析支持以辅助响应生成。此外，对于狼人角色，采用了多种说服策略，包括逻辑诉求、可信度诉求和情感诉求，以有效说服其他玩家配合其行动。
英文摘要：
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.
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。
