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Rubik's Cube (RC) is a well-known and computationally challenging puzzle that has motivated AI researchers to explore efficient alternative representations and problem-solving methods.
ADL and the State-Transition Model of Action
Pednault, E. P. D. 1994 · 1994
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Expressive equivalence of planning formalisms
Bäckström, C. 1995 · 1995
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Finding optimal solutions to Rubik’s Cube using pattern databases
Korf, R. E. 1997 · 1997
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PDDL - The Planning Domain Definition Language
McDermott, D.; Ghallab, M.; Knoblock, C.; Wilkins, D.; Barrett, A.; Christianson, D.; Friedman, M.; Kwok, C.; Golden, K.; Penberthy, S.; Smith, D.; Sun, Y.; and Weld, D. 1998 · 1998
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FF: The fast-forward planning system
Hoffmann, J. 2001 · 2001
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PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains
Fox, M.; and Long, D. 2003 · 2003
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VAL’s Progress: The Automatic Validation Tool for PDDL2.1 used in the International Planning Competition
Howey, R.; and Long, D. 2003 · 2003
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A Planning Heuristic Based on Causal Graph Analysis
Helmert, M. 2004 · 2004
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The fast downward planning system
Helmert, M. 2006 · 2006
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Cube explorer
Kociemba, H. 2006 · 2006
Cited alongside, same era.
Unifying the Causal Graph and Additive Heuristics
Helmert, M.; and Geffner, H. 2008 · 2008
Cited alongside, same era.
Twenty-five moves suffice for Rubik’s cube
Rokicki, T. 2008 · 2008
Cited alongside, same era.
Concise Finite-Domain Representations for PDDL Planning Tasks
Helmert, M. 2009 · 2009
Cited alongside, same era.
Cost-Optimal Planning with Landmarks
Karpas, E.; and Domshlak, C. 2009 · 2009
Cited alongside, same era.
Computing perfect heuristics in polynomial time: On bisimulation and merge-and-shrink abstraction in optimal planning
Nissim, R.; Hoffmann, J.; and Helmert, M. 2011 · 2011
Cited alongside, same era.
Generalized label reduction for merge-and-shrink heuristics
Sievers, S.; Wehrle, M.; and Helmert, M. 2014 · 2014
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Automated planning and acting
Ghallab, M.; Nau, D.; and Traverso, P. 2016 · 2016
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An analysis of merge strategies for merge-and-shrink heuristics
Sievers, S.; Wehrle, M.; and Helmert, M. 2016 · 2016
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Solving the Rubik’s Cube with Approximate Policy Iteration
McAleer, S.; Agostinelli, F.; Shmakov, A.; and Baldi, P. 2018 · 2018
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Code of DeepCubeA
Agostinelli, F. 2019 · 2019
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Solving the Rubik’s Cube Without Human Knowledge
McAleer, S.; Agostinelli, F.; Shmakov, A.; and Baldi, P. 2019 · 2019
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Getting the most out of pattern databases for classical planning
Pommerening, F.; Röger, G.; and Helmert, M. 2013 · 2013
Cited alongside, same era.
Merge-and-shrink abstraction: A method for generating lower bounds in factored state spaces
Helmert, M.; Haslum, P.; Hoffmann, J.; and Nissim, R. 2014 · 2014
Cited alongside, same era.
The diameter of the rubik’s cube group is twenty
Rokicki, T.; Kociemba, H.; Davidson, M.; and Dethridge, J. 2014 · 2014
Cited alongside, same era.
Solving the Rubik’s cube with deep reinforcement learning and search
Agostinelli, F.; McAleer, S.; Shmakov, A.; and Baldi, P. 2019a
Cited in the paper.
Solving the Rubik’s cube with deep reinforcement learning and search
Agostinelli, F.; McAleer, S.; Shmakov, A.; and Baldi, P. 2019b
Cited in the paper.
STRIPS: A new approach to the application of theorem proving to problem solving
Fikes, R. E.; and Nilsson, N. J. 1971a
Cited in the paper.
Saturated cost partitioning for optimal classical planning
Seipp, J.; Keller, T.; and Helmert, M. 2020 · 2020
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A Comparison of Abstraction Heuristics for Rubik’s Cube
Büchner, C.; Ferber, P.; Seipp, J.; and Helmert, M. 2022 · 2022
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