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Despite significant progress in general AI planning, certain domains remain out of reach of current AI planning systems.
Über formal unentscheidbare sätze der principia mathematica und verwandter systeme i
Kurt Gödel · 1931
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Bart Selman, Hector J Levesque, David G Mitchell, et al · 1992
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Tom Bylander · 1994
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Fast planning through planning graph analysis
Avrim L Blum and Merrick L Furst · 1997
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Sokoban is pspace-complete
Joseph Culberson · 1997
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Extending planning graphs to an adl subset
Jana Koehler, Bernhard Nebel, Jörg Hoffmann, and Yannis Dimopoulos · 1997
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Blackbox: A new approach to the application of theorem proving to problem solving
Henry Kautz and Bart Selman · 1998
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Joao P Marques-Silva and Karem A Sakallah · 1999
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Ff: The fast-forward planning system
Jörg Hoffmann · 2001
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Sokoban: Improving the search with relevance cuts
Andreas Junghanns and Jonathan Schaeffer · 2001
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The 2014 international planning competition: Progress and trends
Mauro Vallati, Lukas Chrpa, Marek Grześ, Thomas Leo McCluskey, Mark Roberts, Scott Sanner, et al · 2015
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Deep residual learning for image recognition
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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The international sat solver competitions
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What good are actions? accelerating learning using learned action priors
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Nir Lipovetzky · 2013
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Imagination-augmented agents for deep reinforcement learning
Théophane Weber, Sébastien Racanière, David P Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomenech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, et al · 2017
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Learning generalized reactive policies using deep neural networks
Edward Groshev, Aviv Tamar, Maxwell Goldstein, Siddharth Srivastava, and Pieter Abbeel · 2018
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