Abstract
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.
| Original language | English |
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| Title of host publication | Proceedings of the 12th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016 |
| Editors | Nathan Sturtevant, Brian Magerko |
| Publisher | Association for the Advancement of Artificial Intelligence |
| Pages | 218-224 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781577357728 |
| State | Published - 08 10 2016 |
| Externally published | Yes |
| Event | 12th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016 - Burlingame, United States Duration: 08 10 2016 → 12 10 2016 |
Publication series
| Name | Proceedings - AAAI Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE |
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| ISSN (Print) | 2326-909X |
| ISSN (Electronic) | 2334-0924 |
Conference
| Conference | 12th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2016 |
|---|---|
| Country/Territory | United States |
| City | Burlingame |
| Period | 08/10/16 → 12/10/16 |
Bibliographical note
Publisher Copyright:Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keywords
- believable story generation
- causal story plots
- constrained Monte Carlo Tree Search
- Fabula elements
- user preference