Trustworthiness reasoning is crucial in multiplayer games with incomplete information, enabling agents to identify potential allies and adversaries, thereby enhancing reasoning and decision-making processes. Traditional approaches relying on pre-trained models necessitate extensive domain-specific data and considerable reward feedback, with their lack of real-time adaptability hindering their effectiveness in dynamic environments. In this paper, we introduce the Graph Retrieval Augmented Reasoning (GRATR) framework, leveraging the Retrieval-Augmented Generation (RAG) technique to bolster trustworthiness reasoning in agents. GRATR constructs a dynamic trustworthiness graph, updating it in real-time with evidential information, and retrieves relevant trust data to augment the reasoning capabilities of Large Language Models (LLMs). We validate our approach through experiments on the multiplayer game "Werewolf," comparing GRATR against baseline LLM and LLM enhanced with Native RAG and Rerank RAG. Our results demonstrate that GRATR surpasses the baseline methods by over 30\% in winning rate, with superior reasoning performance. Moreover, GRATR effectively mitigates LLM hallucinations, such as identity and objective amnesia, and crucially, it renders the reasoning process more transparent and traceable through the use of the trustworthiness graph.

翻译：可信度推理在不完全信息多玩家游戏中至关重要，它使智能体能够识别潜在盟友与对手，从而提升推理与决策过程。传统方法依赖预训练模型，需要大量领域特定数据和可观奖励反馈，且缺乏实时适应性，阻碍了其在动态环境中的有效性。本文提出图检索增强推理（GRATR）框架，利用检索增强生成（RAG）技术增强智能体的可信度推理能力。GRATR构建动态可信度图，依据证据信息实时更新，并检索相关可信数据以增强大语言模型（LLMs）的推理能力。我们在多玩家游戏“狼人杀”中通过实验验证了所提方法，将GRATR与基线LLM、以及采用原生RAG和重排序RAG增强的LLM进行对比。结果表明，GRATR在胜率上超越基线方法超过30%，且推理性能更优。此外，GRATR有效缓解了LLM的幻觉问题，如身份与目标遗忘，并通过可信度图的使用，使推理过程更具透明性与可追溯性。