NaRuto: Automatically Acquiring Planning Models from Narrative Texts
Ruiqi Li, Leyang Cui, Songtuan Lin, Patrik Haslum
Abstract
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.
BibTeX
@article{Li_Cui_Lin_Haslum_2024, title={NaRuto: Automatically Acquiring Planning Models from Narrative Texts}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/29999}, DOI={10.1609/aaai.v38i18.29999}, abstractNote={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.}, number={18}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Li, Ruiqi and Cui, Leyang and Lin, Songtuan and Haslum, Patrik}, year={2024}, month={Mar.}, pages={20194-20202} }