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There is a growing interest in applying pre-trained large language models (LLMs) to planning problems.
Strips: A new approach to the application of theorem proving to problem solving
Richard E Fikes and Nils J Nilsson · 1971
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International planning competition, 1998
IPC · 1998
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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
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The ff planning system: Fast plan generation through heuristic search
Jörg Hoffmann and Bernhard Nebel · 2001
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Lpg: A planner based on local search for planning graphs with action costs
Alfonso Gerevini and Ivan Serina · 2002
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Norvig (2003)
Stuart Jonathan Russell · 2003
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Automated Planning: theory and practice
Malik Ghallab, Dana Nau, and Paolo Traverso · 2004
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Val: Automatic plan validation, continuous effects and mixed initiative planning using pddl
Richard Howey, Derek Long, and Maria Fox · 2004
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The fast downward planning system
Malte Helmert · 2006
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Planning domain definition using gipo
Ron M Simpson, Diane E Kitchin, and Thomas Leo McCluskey · 2007
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Learning action models from plan examples using weighted max-sat
Qiang Yang, Kangheng Wu, and Yunfei Jiang · 2007
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Learning complex action models with quantifiers and logical implications
Hankz Hankui Zhuo, Qiang Yang, Derek Hao Hu, and Lei Li · 2010
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Acquiring planning domain models using locm
Stephen N Cresswell, Thomas L McCluskey, and Margaret M West · 2013
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A review of learning planning action models
Ankuj Arora, Humbert Fiorino, Damien Pellier, Marc Métivier, and Sylvie Pesty · 2018
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From skills to symbols: Learning symbolic representations for abstract high-level planning
George Konidaris, Leslie Pack Kaelbling, and Tomas Lozano-Perez · 2018
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Virtualhome: Simulating household activities via programs
Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler, and Antonio Torralba · 2018
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Learning first-order symbolic representations for planning from the structure of the state space
Blai Bonet and Hector Geffner · 2019
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Can transformers reason about effects of actions?
Pratyay Banerjee, Chitta Baral, Man Luo, Arindam Mitra, Kuntal Pal, Tran C Son, and Neeraj Varshney · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Symbolic plans as high-level instructions for reinforcement learning
León Illanes, Xi Yan, Rodrigo Toro Icarte, and Sheila A McIlraith · 2020
Earlier work this paper cites.
–d3wa+–a case study of xaip in a model acquisition task for dialogue planning
Sarath Sreedharan, Tathagata Chakraborti, Christian Muise, Yasaman Khazaeni, and Subbarao Kambhampati · 2020
Earlier work this paper cites.
Discovering underlying plans based on shallow models
Hankz Hankui Zhuo, Yantian Zha, Subbarao Kambhampati, and Xin Tian · 2020
Cited alongside, same era.
Gpt3-to-plan: Extracting plans from text using gpt-3
Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht · 2021
Cited alongside, same era.
Do as i can, not as i say: Grounding language in robotic affordances
Palm-e: An embodied multimodal language model
Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al · 2023
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Reasoning with language model is planning with world model
Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu · 2023
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On the role of large language models in planning, July 2023
Subbarao Kambhampati, Karthik Valmeekam, Matthew Marquez, and Lin Guan · 2023
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Emergent world representations: Exploring a sequence model trained on a synthetic task
Kenneth Li, Aspen K Hopkins, David Bau, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg · 2023
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Text2motion: From natural language instructions to feasible plans
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Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, et al · 2022
Cited alongside, same era.
Macq: A holistic view of model acquisition techniques
Ethan Callanan, Rebecca De Venezia, Victoria Armstrong, Alison Paredes, Tathagata Chakraborti, and Christian Muise · 2022
Cited alongside, same era.
Guided skill learning and abstraction for long-horizon manipulation
Shuo Cheng and Danfei Xu · 2022
Cited alongside, same era.
Leveraging approximate symbolic models for reinforcement learning via skill diversity
Lin Guan, Sarath Sreedharan, and Subbarao Kambhampati · 2022
Cited alongside, same era.
Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch · 2022
Cited alongside, same era.
Inner monologue: Embodied reasoning through planning with language models
Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, et al · 2022
Cited alongside, same era.
Symbols as a lingua franca for bridging human-ai chasm for explainable and advisable ai systems
Subbarao Kambhampati, Sarath Sreedharan, Mudit Verma, Yantian Zha, and Lin Guan · 2022
Cited alongside, same era.
Pre-trained language models for interactive decision-making
Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, et al · 2022
Cited alongside, same era.
Kevin Lin, Christopher Agia, Toki Migimatsu, Marco Pavone, and Jeannette Bohg · 2023
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Llm+ p: Empowering large language models with optimal planning proficiency
Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone · 2023
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Man Luo, Shrinidhi Kumbhar, Mihir Parmar, Neeraj Varshney, Pratyay Banerjee, Somak Aditya, Chitta Baral, et al · 2023
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Faithful chain-of-thought reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch · 2023
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Kolby Nottingham, Prithviraj Ammanabrolu, Alane Suhr, Yejin Choi, Hannaneh Hajishirzi, Sameer Singh, and Roy Fox · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Logic-lm: Empowering large language models with symbolic solvers for faithful logical reasoning
Liangming Pan, Alon Albalak, Xinyi Wang, and William Yang Wang · 2023
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
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Reflexion: an autonomous agent with dynamic memory and self-reflection
Noah Shinn, Beck Labash, and Ashwin Gopinath · 2023
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Gpt-4 doesn’t know it’s wrong: An analysis of iterative prompting for reasoning problems
Kaya Stechly, Matthew Marquez, and Subbarao Kambhampati · 2023
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Can large language models really improve by self-critiquing their own plans?
Karthik Valmeekam, Matthew Marquez, and Subbarao Kambhampati · 2023
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On the planning abilities of large language models–a critical investigation
Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, and Subbarao Kambhampati · 2023
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Zihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang · 2023
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Lionel Wong, Gabriel Grand, Alexander K Lew, Noah D Goodman, Vikash K Mansinghka, Jacob Andreas, and Joshua B Tenenbaum · 2023
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Translating natural language to planning goals with large-language models
Yaqi Xie, Chen Yu, Tongyao Zhu, Jinbin Bai, Ze Gong, and Harold Soh · 2023
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React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao · 2023
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