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We achieved a new milestone in the difficult task of enabling agents to learn about their environment autonomously.
The Chromatic Class of a Multigraph
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A Formal Basis for the Heuristic Determination of Minimum Cost Paths
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STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving
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The symbol grounding problem
Stevan Harnad · 1990
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On the Chromatic Number of Cube-Like Graphs
Charles Payan · 1992
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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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Solving the Symbol Grounding Problem: A Critical Review of Fifteen Years of Research
Mariarosaria Taddeo and Luciano Floridi · 2005
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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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Landmarks, Critical Paths and Abstractions: What’s the Difference Anyway?
Malte Helmert and Carmel Domshlak · 2009
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Plan Recognition as Planning
Miquel Ramírez and Hector Geffner · 2009
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The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks
Silvia Richter and Matthias Westphal · 2010
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Generalised Domain Model Acquisition from Action Traces
Stephen Cresswell and Peter Gregory · 2011
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Potassco: The Potsdam answer set solving collection
Martin Gebser, Benjamin Kaufmann, Roland Kaminski, Max Ostrowski, Torsten Schaub, and Marius Schneider · 2011
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Learning STRIPS Operators from Noisy and Incomplete Observations
Kira Mourão, Luke S. Zettlemoyer, Ronald P. A. Petrick, and Mark Steedman · 2012
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Estimating or Propagating Gradients through Stochastic Neurons for Conditional Computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Acquiring planning domain models using LOCM
Stephen Cresswell, Thomas Leo McCluskey, and Margaret Mary West · 2013
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Approximation Algorithms
Vijay V Vazirani · 2013
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Action-Model Acquisition from Noisy Plan Traces
Hankz Hankui Zhuo and Subbarao Kambhampati · 2013
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Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Merge-and-Shrink Abstraction: A Method for Generating Lower Bounds in Factored State Spaces
Malte Helmert, Patrik Haslum, Jörg Hoffmann, and Raz Nissim · 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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Contingent versus Deterministic Plans in Multi-Modal Journey Planning
A. Botea and S. Braghin · 2015
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Exploiting Block Deordering for Improving Planners Efficiency
Lukáš Chrpa and Fazlul Hasan Siddiqui · 2015
Neural Discrete Representation Learning
Aaron van den Oord, Oriol Vinyals, et al · 2017
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Learning STRIPS Action Models with Classical Planning
Diego Aineto, Sergio Jiménez, and Eva Onaindia · 2018
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Goal Recognition in Latent Space
Leonardo Amado, Ramon Fraga Pereira, João Paulo Aires, Mauricio Cecilio Magnaguagno, Roger Granada, and Felipe Meneguzzi · 2018
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Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary
Masataro Asai and Alex Fukunaga · 2018
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Extracting Action Sequences from Texts Based on Deep Reinforcement Learning
Wenfeng Feng, Hankz Hankui Zhuo, and Subbarao Kambhampati · 2018
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From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning
George Konidaris, Leslie Pack Kaelbling, and Tomas Lozano-Perez · 2018
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On the Online Generation of Effective Macro-Operators
Lukáš Chrpa, Mauro Vallati, and Thomas L. McCluskey · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe and Christian Szegedy · 2015
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Symbol Acquisition for Probabilistic High-Level Planning
George Konidaris, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2015
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Human-Level Control through Deep Reinforcement Learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, et al · 2015
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Mastering the Game of Go with Deep Neural Networks and Tree Search
David Silver, Aja Huang, Chris J Maddison, et al · 2016
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Learning Plannable Representations with Causal InfoGAN
Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart Russell, and Pieter Abbeel · 2018
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DVAE#: Discrete variational autoencoders with relaxed Boltzmann priors
Arash Vahdat, Evgeny Andriyash, and William Macready · 2018
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DVAE++: Discrete variational autoencoders with overlapping transformations
Arash Vahdat, William G Macready, Zhengbing Bian, Amir Khoshaman, and Evgeny Andriyash · 2018
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Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder
Masataro Asai and Hiroshi Kajino · 2019
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Neural-Symbolic Descriptive Action Model from Images: The Search for STRIPS
Masataro Asai · 2019
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Unsupervised Grounding of Plannable First-Order Logic Representation from Images
Masataro Asai · 2019
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Neural Logic Machines
Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, and Denny Zhou · 2019
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An Introduction to the Planning Domain Definition Language
Patrik Haslum, Nir Lipovetzky, Daniele Magazzeni, and Christian Muise · 2019
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Learning Finite State Representations of Recurrent Policy Networks
Anurag Koul, Alan Fern, and Sam Greydanus · 2019
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On the Variance of the Adaptive Learning Rate and Beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, et al · 2019
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Learning Action Models from Disordered and Noisy Plan Traces
Hankz Hankui Zhuo, Jing Peng, and Subbarao Kambhampati · 2019
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Learning First-Order Symbolic Representations for Planning from the Structure of the State Space
Blai Bonet and Hector Geffner · 2020
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