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Symbolic planning models allow decision-making agents to sequence actions in arbitrary ways to achieve a variety of goals in dynamic domains.
STRIPS: A new approach to the application of theorem proving to problem solving
Richard E Fikes and Nils J Nilsson. 1971 · 1971
Earlier work this paper cites.
Explanation-based generalization: A unifying view
Tom M Mitchell, Richard M Keller, and Smadar T Kedar-Cabelli. 1986 · 1986
Earlier work this paper cites.
Learning from the environment based on percepts and actions
Wei-Min Shen. 1989 · 1989
Earlier work this paper cites.
Rule Creation and Rule Learning Through Environmental Exploration.. In IJCAI . Citeseer, 675–680
Wei-Min Shen and Herbert A Simon. 1989 · 1989
Earlier work this paper cites.
Q-learning
Christopher JCH Watkins and Peter Dayan. 1992 · 1992
Earlier work this paper cites.
Learning by experimentation: Incremental refinement of incomplete planning domains
Yolanda Gil. 1994 · 1994
Earlier work this paper cites.
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh. 1999 · 1999
Earlier work this paper cites.
Learning-assisted automated planning: looking back, taking stock, going forward
Terry Zimmerman and Subbarao Kambhampati. 2003 · 2003
Earlier work this paper cites.
Combining reinforcement learning with symbolic planning
Matthew Grounds and Daniel Kudenko. 2005 · 2005
Earlier work this paper cites.
Learning action models from plan examples using weighted MAX-SAT
Qiang Yang, Kangheng Wu, and Yunfei Jiang. 2007 · 2007
Earlier work this paper cites.
Plan-based reward shaping for reinforcement learning. In 2008 4th International IEEE Conference Intelligent Systems , Vol. 2. IEEE, 10–22
Marek Grzes and Daniel Kudenko. 2008 · 2008
Earlier work this paper cites.
The sketching approach to program synthesis. In Asian Symposium on Programming Languages and Systems . Springer, 4–13
Armando Solar-Lezama. 2009 · 2009
Earlier work this paper cites.
Learning methods to generate good plans: Integrating htn learning and reinforcement learning. In Twenty-Fourth AAAI Conference on Artificial Intelligence . Citeseer
Chad Hogg, Ugur Kuter, and Hector Munoz-Avila. 2010 · 2010
Cited alongside, same era.
Planning for human-robot teaming in open worlds
Kartik Talamadupula, J Benton, Subbarao Kambhampati, Paul Schermerhorn, and Matthias Scheutz. 2010 · 2010
Cited alongside, same era.
A review of machine learning for automated planning
Sergio Jiménez, Tomás De La Rosa, Susana Fernández, Fernando Fernández, and Daniel Borrajo. 2012 · 2012
Cited alongside, same era.
Abstract planning for reactive robots. In 2012 IEEE International Conference on Robotics and Automation . IEEE, 4379–4384
Saket Joshi, Paul Schermerhorn, Roni Khardon, and Matthias Scheutz. 2012 · 2012
Cited alongside, same era.
Acquiring planning domain models using LOCM
Stephen N Cresswell, Thomas Leo McCluskey, and Margaret M West. 2013 · 2013
Cited alongside, same era.
From skills to symbols: Learning symbolic representations for abstract high-level planning
George Konidaris, Leslie Pack Kaelbling, and Tomas Lozano-Perez. 2018 · 2018
Later among the works it cites.
MacGyver problems: Ai challenges for testing resourcefulness and creativity
Vasanth Sarathy and Matthias Scheutz. 2018 · 2018
Later among the works it cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto. 2018 · 2018
Later among the works it cites.
Fangkai Yang, Daoming Lyu, Bo Liu, and Steven Gustafson. 2018 · 2018
Later among the works it cites.
Learning STRIPS action models with classical planning
Diego Aineto, Sergio Jiménez, and Eva Onaindia. 2019 · 2019
Later among the works it cites.
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Planning with Partially Specified Behaviors.. In CCIA . 263–272
Javier Segovia Aguas, Jonathan Ferrer-Mestres, and Anders Jonsson. 2016 · 2016
Cited alongside, same era.
Automated planning and acting
Malik Ghallab, Dana Nau, and Paolo Traverso. 2016 · 2016
Cited alongside, same era.
A synthesis of automated planning and reinforcement learning for efficient, robust decision-making
Matteo Leonetti, Luca Iocchi, and Peter Stone. 2016 · 2016
Cited alongside, same era.
Classical planning in deep latent space: Bridging the subsymbolic-symbolic boundary
Masataro Asai and Alex Fukunaga. 2017 · 2017
Cited alongside, same era.
A review of learning planning action models
Ankuj Arora, Humbert Fiorino, Damien Pellier, Marc Métivier, and Sylvie Pesty. 2018 · 2018
Cited alongside, same era.
Minimalistic Gridworld Environment for OpenAI Gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal. 2018 · 2018
Cited alongside, same era.
Learning to Plan from Raw Data in Grid-based Games.. In GCAI . 54–67
Andrea Dittadi, Thomas Bolander, and Ole Winther. 2018 · 2018
Cited alongside, same era.
Creative Problem Solving by Robots Using Action Primitive Discovery. In 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) . IEEE, 228–233
Evana Gizzi, Mateo Guaman Castro, and Jivko Sinapov. 2019 · 2019
Later among the works it cites.
SDRL: interpretable and data-efficient deep reinforcement learning leveraging symbolic planning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 2970–2977
Daoming Lyu, Fangkai Yang, Bo Liu, and Steven Gustafson. 2019 · 2019
Later among the works it cites.
Incremental Learning of Planning Actions in Model-Based Reinforcement Learning.. In IJCAI . 3195–3201
Jun Hao Alvin Ng and Ronald PA Petrick. 2019 · 2019
Later among the works it cites.
Planning with Abstract Learned Models While Learning Transferable Subtasks
John Winder, Stephanie Milani, Matthew Landen, Erebus Oh, Shane Parr, Shawn Squire, Marie desJardins, and Cynthia Matuszek. 2019 · 2019
Later among the works it cites.
Representation Learning for Classical Planning from Partially Observed Traces
Zhanhao Xiao, Hai Wan, Hankui Hankz Zhuo, Jinxia Lin, and Yanan Liu. 2019 · 2019
Later among the works it cites.
Symbolic Plans as High-Level Instructions for Reinforcement Learning. In Proceedings of the International Conference on Automated Planning and Scheduling , Vol. 30. 540–550
León Illanes, Xi Yan, Rodrigo Toro Icarte, and Sheila A McIlraith. 2020 · 2020
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Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, and Peter Stone. 2020 · 2020
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