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Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck.
Statistical theory of extreme values and some practical applications: a series of lectures
Emil Julius Gumbel and Julius Lieblein · 1954
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The knowledge acquisition bottleneck: Time for reassessment?
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Complexity Results for SAS+ Planning
Christer Bäckström and Bernhard Nebel · 1995
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Gradient-Based Learning Applied to Document Recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Subset of PDDL for the AIPS2000 Planning Competition
Fahiem Bacchus · 2000
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The 1998 AI Planning Systems Competition
Drew V. McDermott · 2000
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The FF Planning System: Fast Plan Generation through Heuristic Search
Jörg Hoffmann and Bernhard Nebel · 2001
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A Planning Heuristic Based on Causal Graph Analysis
Malte Helmert · 2004
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The Fast Downward Planning System
Malte Helmert · 2006
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Reducing the Dimensionality of Data with Neural Networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
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Flexible Abstraction Heuristics for Optimal Sequential Planning
Malte Helmert, Patrik Haslum, and Jörg Hoffmann · 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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Learning Classifiers from Only Positive and Unlabeled Data
Charles Elkan and Keith Noto · 2008
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The Symbol Grounding Problem has been Solved. So What’s Next?
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Malte Helmert and Carmel Domshlak · 2009
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Goal Distance Estimation for Automated Planning using Neural Networks and Support Vector Machines
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Adam: A Method for Stochastic Optimization
Diederik Kingma and Jimmy Ba · 2014
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Semi-Supervised Learning with Deep Generative Models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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Constructing Symbolic Representations for High-Level Planning
George Konidaris, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2014
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A* sampling
Chris J Maddison, Daniel Tarlow, and Tom Minka · 2014
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Solving Large-Scale Planning Problems by Decomposition and Macro Generation
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Hybrid Computing using a Neural Network with Dynamic External Memory
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Purely Declarative Action Representations are Overrated: Classical Planning with Simulators
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Categorical Reparameterization with Gumbel-Softmax
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Framer: Planning Models from Natural Language Action Descriptions
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