NaRuto: Automatically Acquiring Planning Models from Narrative Texts
DOI:
https://doi.org/10.1609/aaai.v38i18.29999Keywords:
PRS: Learning for Planning and SchedulingAbstract
Domain model acquisition has been identified as a bottleneck in the application of planning technology, especially within narrative planning. Learning action models from narrative texts in an automated way is essential to overcome this barrier, but challenging because of the inherent complexities of such texts. We present an evaluation of planning domain models derived from narrative texts using our fully automated, unsupervised system, NaRuto. Our system combines structured event extraction, predictions of commonsense event relations, and textual contradictions and similarities. Evaluation results show that NaRuto generates domain models of significantly better quality than existing fully automated methods, and even sometimes on par with those created by semi-automated methods, with human assistance.Downloads
Published
2024-03-24
How to Cite
Li, R., Cui, L., Lin, S., & Haslum, P. (2024). NaRuto: Automatically Acquiring Planning Models from Narrative Texts. Proceedings of the AAAI Conference on Artificial Intelligence, 38(18), 20194-20202. https://doi.org/10.1609/aaai.v38i18.29999
Issue
Section
AAAI Technical Track on Planning, Routing, and Scheduling