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Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents.
Some Philosophical Problems From The Standpoint of Artificial Intelligence
J. McCarthy and P. Hayes · 1969
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A. Schwartz · 1993
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S. Mahadevan · 1996
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Action languages
M. Gelfond and V. Lifschitz · 1998
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Pddl-the planning domain definition language
D. McDermott, M. Ghallab, A. Howe, C. Knoblock, A. Ram, M. Veloso, D. Weld, and D. Wilkins · 1998
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Reinforcement learning: An introduction
R. S. Sutton and A. G. Barto · 1998
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R. Parr and S. J. Russell · 1998
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Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
R. S. Sutton, D. Precup, and S. Singh · 1999
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Using abstract models of behaviours to automatically generate reinforcement learning hierarchies
M. R. Ryan · 2002
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Recent advances in hierarchical reinforcement learning
A. G. Barto and S. Mahadevan · 2003
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Dynamic abstraction in reinforcement learning via clustering
S. Mannor, I. Menache, A. Hoze, and U. Klein · 2004
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The fast downward planning system
M. Helmert · 2006
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Automated planning
A. Cimatti, M. Pistore, and P. Traverso · 2008
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What is answer set programming?
V. Lifschitz · 2008
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Learning methods to generate good plans: Integrating htn learning and reinforcement learning
C. Hogg, U. Kuter, and H. Munoz-Avila · 2010
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Conflict-driven answer set solving: From theory to practice
M. Gebser, B. Kaufmann, and T. Schaub · 2012
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Robot task planning and explanation in open and uncertain worlds
M. Hanheide, M. Göbelbecker, G. S Horn, et al · 2015
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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, G. Ostrovski, et al · 2015
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Planning with task-oriented knowledge acquisition for a service robot
K. Chen, F. Yang, and X. Chen · 2016
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Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation
T. D. Kulkarni, K. Narasimhan, A. Saeedi, and J. Tenenbaum · 2016
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A synthesis of automated planning and reinforcement learning for efficient, robust decision-making
M. Leonetti, L. Iocchi, and P. Stone · 2016
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Clingcon: The next generation
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M. Leonetti, L. Iocchi, and F. Patrizi · 2012
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Integrating planning, execution, and learning to improve plan execution
S. Jiménez, F. Fernández, and D. Borrajo · 2013
Cited alongside, same era.
Action Language ℬ 𝒞 \mathcal{BC} : A Preliminary Report
J. Lee, V. Lifschitz, and F. Yang · 2013
Cited alongside, same era.
Planning in Action Language ℬ 𝒞 \mathcal{BC} while Learning Action Costs for Mobile Robots
P. Khandelwal, F. Yang, M. Leonetti, V. Lifschitz, and P. Stone · 2014
Cited alongside, same era.
M. Banbara, B. Kaufmann, M. Ostrowski, and T. Schaub · 2017
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Bwibots: A platform for bridging the gap between ai and human–robot interaction research
P. Khandelwal, S. Zhang, J. Sinapov, M. Leonetti, J. Thomason, F. Yang, I. Gori, M. Svetlik, P. Khante, and V. Lifschitz, J. Aggarwal, R. Mooney, P. Stone · 2017
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A laplacian framework for option discovery in reinforcement learning
M. C Machado, M. G Bellemare, and M. Bowling · 2017
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Dynamically constructed (po) mdps for adaptive robot planning
S. Zhang, P. Khandelwal, and P. Stone · 2017
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