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Although there have been approaches that are capable of learning action models from plan traces, there is no work on learning action models from textual observations, which is pervasive and much easier to collect from real-world applications compared to plan traces.
Pddl-the planning domain definition language
Drew McDermott, Malik Ghallab, Adele E. Howe, Craig A. Knoblock, Ashwin Ram, Manuela M. Veloso, Daniel S. Weld, and David E. Wilkins · 1998
Earlier work this paper cites.
The fast downward planning system
Malte Helmert · 2006
Earlier work this paper cites.
Learning action models from plan examples using weighted max-sat
Qiang Yang, Kangheng Wu, and Yunfei Jiang · 2007
Earlier work this paper cites.
Extracting STRIPS representations of actions and events
Avirup Sil and Alexander Yates · 2011
Earlier work this paper cites.
Learning STRIPS operators from noisy and incomplete observations
Kira Mourão, Luke S. Zettlemoyer, Ronald P. A. Petrick, and Mark Steedman · 2012
Earlier work this paper cites.
Acquiring planning domain models using LOCM
Stephen Cresswell, Thomas Leo McCluskey, and Margaret Mary West · 2013
Earlier work this paper cites.
Action-model acquisition from noisy plan traces
Hankz Hankui Zhuo and Subbarao Kambhampati · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
The stanford corenlp natural language processing toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Rose Finkel, Steven Bethard, and David McClosky · 2014
Cited alongside, same era.
Learning models of human behaviour from textual instructions
Kristina Y. Yordanova and Thomas Kirste · 2016
Cited alongside, same era.
Storyframer: From input stories to output planning models
Thomas Hayton, Julie Porteous, Joao Ferreira, Alan Lindsay, and Jonathon Read · 2017
Cited alongside, same era.
Framer: Planning models from natural language action descriptions
Alan Lindsay, Jonathon Read, João F. Ferreira, Thomas Hayton, Julie Porteous, and Peter Gregory · 2017
Cited alongside, same era.
Model-lite planning: Case-based vs. model-based approaches
Hankz Hankui Zhuo and Subbarao Kambhampati · 2017
Cited alongside, same era.
Extracting action sequences from texts based on deep reinforcement learning
Plan-and-write: Towards better automatic storytelling
Lili Yao, Nanyun Peng, Ralph M. Weischedel, Kevin Knight, Dongyan Zhao, and Rui Yan · 2019
Later among the works it cites.
Learning action models from disordered and noisy plan traces
Hankz Hankui Zhuo, Jing Peng, and Subbarao Kambhampati · 2019
Later among the works it cites.
Topic modeling in embedding spaces
Adji Bousso Dieng, Francisco J. R. Ruiz, and David M. Blei · 2020
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Narrative planning model acquisition from text summaries and descriptions
Thomas Hayton, Julie Porteous, João Fernando Ferreira, and Alan Lindsay · 2020
Later among the works it cites.
DYPLOC: Dynamic planning of content using mixed language models for text generation
Xinyu Hua, Ashwin Sreevatsa, and Lu Wang · 2021
Later among the works it cites.
Stylized story generation with style-guided planning
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Wenfeng Feng, Hankz Hankui Zhuo, and Subbarao Kambhampati · 2018
Cited alongside, same era.
A skeleton-based model for promoting coherence among sentences in narrative story generation
Jingjing Xu, Xuancheng Ren, Yi Zhang, Qi Zeng, Xiaoyan Cai, and Xu Sun · 2018
Cited alongside, same era.
Learning action models with minimal observability
Diego Aineto, Sergio Jiménez Celorrio, and Eva Onaindia · 2019
Cited alongside, same era.
Xiangzhe Kong, Jialiang Huang, Ziquan Tung, Jian Guan, and Minlie Huang · 2021
Later among the works it cites.
Online learning of action models for PDDL planning
Leonardo Lamanna, Alessandro Saetti, Luciano Serafini, Alfonso Gerevini, and Paolo Traverso · 2021
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Content learning with structure-aware writing: A graph-infused dual conditional variational autoencoder for automatic storytelling
Meng-Hsuan Yu, Juntao Li, Zhangming Chan, Rui Yan, and Dongyan Zhao · 2021
Later among the works it cites.