使用约束蒙特卡罗树搜索生成具有用户偏好的可信因果图

Proceedings. AAAI Artificial Intelligence and Interactive Digital Entertainment Conference Pub Date : 2021-06-25 DOI:10.1609/aiide.v12i1.12875

V. Soo, Chi-Mou Lee, T. Chen

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引用次数: 6

摘要

我们通过从现有的常识本体ConceptNet5中提取因果链接，构建了基于Fabula元素的大规模因果知识。我们设计了一个约束蒙特卡罗树搜索(cMCTS)算法，允许用户指定在生成的故事中出现的积极和消极概念。cMCTS可以找到一个可信的因果故事情节。我们通过实验证明了其优点，并讨论了在cMCTS中可能产生不连贯因果图的补救策略。

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Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search

We construct a large scale of causal knowledge in term of Fabula elements by extracting causal links from existing common sense ontology ConceptNet5. We design a Constrained Monte Carlo Tree Search (cMCTS) algorithm that allows users to specify positive and negative concepts to appear in the generated stories. cMCTS can find a believable causal story plot. We show the merits by experiments and discuss the remedy strategies in cMCTS that may generate incoherent causal plots.

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Proceedings. AAAI Artificial Intelligence and Interactive Digital Entertainment Conference

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