Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search
DOI:
https://doi.org/10.1609/aiide.v12i1.12875Keywords:
Fabula elements, causal story plots, constrained Monte Carlo Tree Search, user preference, believable story generationAbstract
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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Published
2021-06-25
How to Cite
Soo, V.-W., Lee, C.-M., & Chen, T.-H. (2021). Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 12(1), 218-224. https://doi.org/10.1609/aiide.v12i1.12875
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Section
Poster Papers