constrained monte carlo tree search
Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search
Soo, Von-Wun (National Tsing Hua University) | Lee, Chi-Mou (National Tsing Hua University) | Chen, Tai-Hsun (National Tsing Hua University)
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.