Salience Vectors for Measuring Distance between Stories
Salience Vectors for Measuring Distance between Stories
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DOI：
10.1609/aiide.v18i1.21952
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Rachelyn Farrell;Mira Fisher;Stephen G. Ware
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文献类型：
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作者：
Rachelyn Farrell;Mira Fisher;Stephen G. Ware
在给定的故事领域模型中，叙事规划者生成代表故事情节的行动序列。这是为交互式叙事系统创建分支故事的有效方法，这种系统可以在不同内容的多个故事线之间保持逻辑一致性。我们需要故事比较技术，它可以让像经验管理器和领域创作工具这样的系统推断出多个故事或分支之间的异同。我们提出了一种基于人类故事感知的认知模型，将叙事计划总结为数字向量的算法。这些向量编码了重要的故事信息，并可以使用标准距离函数进行比较，以量化两个故事之间的总体语义差异。我们证明了这种距离度量基于人类对故事相似性的注释是高度准确的，并将其与几种替代方法进行了比较。我们还探索了方法的变体，试图将其应用于其他类型的故事系统。
Narrative planners generate sequences of actions that represent story plots given a story domain model. This is a useful way to create branching stories for interactive narrative systems that maintain logical consistency across multiple storylines with different content. There is a need for story comparison techniques that can enable systems like experience managers and domain authoring tools to reason about similarities and differences between multiple stories or branches. We present an algorithm for summarizing narrative plans as numeric vectors based on a cognitive model of human story perception. The vectors encode important story information and can be compared using standard distance functions to quantify the overall semantic difference between two stories. We show that this distance metric is highly accurate based on human annotations of story similarity, and compare it to several alternative approaches. We also explore variations of our method in an attempt to broaden its applicability to other types of story systems.