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In this paper we explore the theoretical boundaries of planning in a setting where no model of the agent's actions is given.
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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On the uniform convergence of relative frequencies of events to their probabilities
V. Vapnik and A. Chervonenkis · 1971
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A theory of the learnable
Leslie G. Valiant · 1984
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Cryptographic limitations on learning boolean formulae and finite automata
Michael Kearns and Leslie Valiant · 1994
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Memoryless policies: Theoretical limitations and practical results
Michael L. Littman · 1994
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Learning planning operators by observation and practice
Xuemei Wang · 1994
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Complexity results for SAS+ planning
Christer Bäckström and Bernhard Nebel · 1995
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Learning by observation and practice: An incremental approach for planning operator acquisition
Xuemei Wang · 1995
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PDDL-the planning domain definition language
Drew McDermott, Malik Ghallab, Adele Howe, Craig Knoblock, Ashwin Ram, Manuela Veloso, Daniel Weld, and David Wilkins · 1998
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Solving POMDPs by searching the space of finite policies
Nicolas Meuleau, Kee-Eung Kim, Leslie Pack Kaelbling, and Anthony R. Cassandra · 1999
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On the compilability and expressive power of propositional planning formalisms
Bernhard Nebel · 2000
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A sparse sampling algorithm for near-optimal planning in large Markov decision processes
Michael Kearns, Yishay Mansour, and Andrew Ng · 2002
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On the sample complexity of reinforcement learning
Sham M. Kakade · 2003
Nanorobot architecture for medical target identification
Adriano Cavalcanti, Bijan Shirinzadeh, Robert A Freitas Jr, and Tad Hogg · 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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Efficient learning of action schemas and web-service descriptions
Thomas J. Walsh and Michael L. Littman · 2008
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Learning STRIPS operators from noisy and incomplete observations
Kira Mourão, Luke S Zettlemoyer, Ronald Petrick, and Mark Steedman · 2012
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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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Integrating planning, execution, and learning to improve plan execution
Sergio Jiménez, Fernando Fernández, and Daniel Borrajo · 2013
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Explanation-based acquisition of planning operators
Geoffrey Levine and Gerald DeJong · 2006
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Complexity theoretic limtations on learning DNF’s
Amit Daniely and Shai Shalev-Shwartz · 2016
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