通过结构化知识树在LLM驱动的侦探游戏中强制执行叙事可靠性与认知节奏
Enforcing Narrative Reliability and Epistemic Pacing in LLM-Driven Detective Games via Structured Knowledge Trees
- University of Calgary（卡尔加里大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出结构化知识树与三智能体LLM流水线，在侦探游戏中减少64.78%关键幻觉并完全防止过早披露，确保叙事可靠性与认知节奏。
AI中文摘要：
大型语言模型（LLMs）使得交互式游戏中的开放式对话成为可能，但其非确定性输出使得保持作者控制、事实一致性以及预期的信息披露顺序变得困难。这些挑战在侦探游戏中尤为显著，因为过早的揭示或捏造的细节可能破坏玩家推进逻辑。我们提出了一种结构化知识树架构，并配合三智能体LLM流水线，用于控制开放式审讯游戏中的对话。该系统将知识检索、对话生成和响应验证分离，以确保虚拟嫌疑人仅透露当前叙事状态所允许的信息。我们通过可游玩的侦探游戏测试平台《Adrian Gale的审讯》以及一项正式的用户研究来评估该方法，该研究考察了幻觉减少、对作者设定的披露序列的遵循以及感知到的逻辑推进。我们的结果表明，该结构化架构将关键幻觉减少了64.78%，并完全防止了过早的叙事披露。虽然严格的机械约束在强制对话揭示方面引入了可用性权衡，但该系统成功执行了严格的认知节奏，并为玩家提供了解决案件推进的清晰、主观感受。
英文摘要：
Large Language Models (LLMs) enable open-ended dialogue in interactive games, but their non-deterministic outputs make it difficult to preserve authorial control, factual consistency, and the intended sequence of information disclosure. These challenges are particularly significant in detective games, where premature revelation or fabricated details can undermine the logic of player progression. We present a Structured Knowledge Tree architecture coupled with a tri-agent LLM pipeline for controlling dialogue in an open-ended interrogation game. The system separates knowledge retrieval, dialogue generation, and response verification to ensure that the virtual suspect reveals only information permitted by the current narrative state. We evaluate the approach through The Interrogation of Adrian Gale, a playable detective-game testbed, and a formal user study examining hallucination reduction, adherence to authored disclosure sequences, and perceived logical progression. Our results demonstrate that the structured architecture reduces critical hallucinations by 64.78% and entirely prevents premature narrative disclosure. While the strict mechanical constraints introduced usability trade-offs regarding forced conversational reveals, the system successfully enforces rigorous epistemic pacing and provides players with a clear, subjective sense of progression toward solving the case.
