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Extracting action sequences from natural language texts is challenging, as it requires commonsense inferences based on world knowledge.
STRIPS: A new approach to the application of theorem proving to problem solving
Richard Fikes and Nils J. Nilsson · 1971
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Walk the talk: Connecting language, knowledge, and action in route instructions
Matt Macmahon, Brian Stankiewicz, and Benjamin Kuipers · 2006
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Reinforcement learning for mapping instructions to actions
S. R. K. Branavan, Harr Chen, Luke S. Zettlemoyer, and Regina Barzilay · 2009
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Acquisition of object-centred domain models from planning examples
Stephen Cresswell, Thomas Leo McCluskey, and Margaret Mary West · 2009
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Extracting action and event semantics from web text
Avirup Sil, Fei Huang, and Alexander Yates · 2010
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Learning to interpret natural language navigation instructions from observations
David L. Chen and Raymond J. Mooney · 2011
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Extracting STRIPS representations of actions and events
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Fast online lexicon learning for grounded language acquisition
David Chen · 2012
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Adapting discriminative reranking to grounded language learning
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Unsupervised pcfg induction for grounded language learning with highly ambiguous supervision
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Learning hierarchical task network domains from partially observed plan traces
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Human-level control through deep reinforcement learning
V Mnih, K Kavukcuoglu, D Silver, A. A. Rusu, J Veness, M. G. Bellemare, A Graves, M Riedmiller, A. K. Fidjeland, and G Ostrovski · 2015
End-to-end sequence labeling via bi-directional lstm-cnns-crf
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Listen, attend, and walk: neural mapping of navigational instructions to action sequences
Hongyuan Mei, Mohit Bansal, and Matthew R. Walter · 2016
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Navigational instruction generation as inverse reinforcement learning with neural machine translation
Andrea F Daniele, Mohit Bansal, and Matthew R Walter · 2017
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Framer: Planning models from natural language action descriptions
Alan Lindsay, Jonathon Read, João F. Ferreira, Thomas Hayton, Julie Porteous, and Peter Gregory · 2017
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From natural language instructions to structured robot plans
Mihai Pomarlan, Sebastian Koralewski, and Michael Beetz · 2017
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Language understanding for text-based games using deep reinforcement learning
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Ye Zhang and Byron C. Wallace · 2015
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Nils Reimers and Iryna Gurevych · 2017
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Combining knowledge with deep convolutional neural networks for short text classification
Jin Wang, Zhongyuan Wang, Dawei Zhang, and Jun Yan · 2017
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Model-lite planning: Case-based vs. model-based approaches
Hankz Hankui Zhuo and Subbarao Kambhampati · 2017
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