用规则化状态变换提升对话叙事一致性与玩家创造力。
World-State Transformations for Neuro-symbolic Interactive Storytelling
- 结合神经符号架构，用预设规则触发世界状态变化。
- 玩家在双语环境下表现更连贯的创意互动，故事一致性提升。
- 适合研究叙事系统设计、人机共创故事的开发者与研究者。
大型语言模型（LLMs）改变了可处理自由文本用户输入的交互式叙事系统可能性。然而，随着这类系统增多，仅依赖LLMs导致的故事不连贯问题日益凸显。近期研究表明，LLMs能有效预测基于规则的交互式叙事系统中的状态变化，从而触发预编程的世界状态转换。本文探索此类转换是否能在缓解纯LLM方法常见不连贯性的同时，成为激发玩家表达的催化剂。基于神经符号架构，我们使用开源模型Llama 3 70B和闭源模型Gemini 1.5 Flash，在英语和西班牙语环境中进行实验。八名参与者体验了两个精心设计的场景，以评估不同目标。观察结果表明，状态转换有助于维持世界状态的一致性，同时鼓励玩家通过书面输入进行创造性互动。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have changed the possibilities of Interactive Storytelling systems that process free-text user input. However, as more of these systems are built, evidence continues to mount regarding the story coherence problems that arise when relying solely on them. Recent research suggests that LLMs can effectively predict state changes within rule-based Interactive Storytelling systems, triggering pre-programmed world-state transformations. In this paper, we conduct an exploratory evaluation of whether such transformations can serve as a catalyst for player expression while aiming to address the incoherence issues typical of purely LLM-based approaches. Building upon a neuro-symbolic architecture, we conducted experiments using an open-source model (Llama 3 70B) and a closed-source model (Gemini 1.5 Flash), with testing conducted in both English and Spanish. Eight participants played two scenarios, carefully designed to assess different evaluation objectives. Our observations suggest that transformations offer a way to maintain world-state consistency while encouraging players to interact creatively through their written inputs.
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