Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search
نویسندگان
چکیده
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. keywords: Fabula elements, causal story plots, constrained Monte Carlo Tree Search, user preference, believable story generation
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