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Learning how the world works is central to building AI agents that can adapt to complex environments.
Optimal control of markov processes with incomplete state information i
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Universal artificial intelligence: Sequential decisions based on algorithmic probability
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Efficient selectivity and backup operators in monte-carlo tree search
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Hierarchical task and motion planning in the now
Leslie Pack Kaelbling and Tomás Lozano-Pérez · 2011
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Predictive state representations: A new theory for modeling dynamical systems
Satinder Singh, Michael James, and Matthew Rudary · 2012
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A survey of monte carlo tree search methods
Cameron B Browne, Edward Powley, Daniel Whitehouse, Simon M Lucas, Peter I Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton · 2012
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The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
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Neural mechanisms of hierarchical planning in a virtual subway network
Jan Balaguer, Hugo Spiers, Demis Hassabis, and Christopher Summerfield · 2016
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Human learning in atari
Pedro Tsividis, Thomas Pouncy, Jaqueline L Xu, Joshua B Tenenbaum, and Samuel J Gershman · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Schema networks: Zero-shot transfer with a generative causal model of intuitive physics
Ken Kansky, Tom Silver, David A Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, Scott Phoenix, and Dileep George · 2017
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The option-critic architecture
Pierre-Luc Bacon, Jean Harb, and Doina Precup · 2017
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Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
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David Ha and Jürgen Schmidhuber · 2018
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Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents
Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew J. Hausknecht, and Michael Bowling · 2018
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Pytorch: An imperative style, high-performance deep learning library
A Paszke · 2019
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People infer recursive visual concepts from just a few examples
Brenden M Lake and Steven T Piantadosi · 2020
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Learning abstract structure for drawing by efficient motor program induction
Lucas Tian, Kevin Ellis, Marta Kryven, and Josh Tenenbaum · 2020
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Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2020
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Model based reinforcement learning for atari
Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Miłos, Błażej Osiński, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, and Henryk Michalewski · 2020
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Event perception and memory
Jeffrey M Zacks · 2020
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Adventures in flatland: Perceiving social interactions under physical dynamics
Neurosymbolic grounding for compositional world models, 2023
Atharva Sehgal, Arya Grayeli, Jennifer J. Sun, and Swarat Chaudhuri · 2023
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Linlu Qiu, Liwei Jiang, Ximing Lu, Melanie Sclar, Valentina Pyatkin, Chandra Bhagavatula, Bailin Wang, Yoon Kim, Yejin Choi, Nouha Dziri, and Xiang Ren · 2023
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Compositional Diffusion-Based Continuous Constraint Solvers
Zhutian Yang, Jiayuan Mao, Yilun Du, Jiajun Wu, Joshua B. Tenenbaum, Tomás Lozano-Pérez, and Leslie Pack Kaelbling · 2023
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Worldcoder, a model-based LLM agent: Building world models by writing code and interacting with the environment
Hao Tang, Darren Yan Key, and Kevin Ellis · 2024
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Automated discovery of symbolic laws governing skill acquisition from naturally occurring data
Sannyuya Liu, Qing Li, Xiaoxuan Shen, Jianwen Sun, and Zongkai Yang · 2024
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Tianmin Shu, Marta Kryven, Tomer D Ullman, and Joshua B Tenenbaum · 2020
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Compositional visual generation with energy based models
Yilun Du, Shuang Li, and Igor Mordatch · 2020
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Learning evolved combinatorial symbols with a neuro-symbolic generative model
Matthias Hofer, Tuan Anh Le, Roger Levy, and Josh Tenenbaum · 2021
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First return, then explore
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O Stanley, and Jeff Clune · 2021
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Learning symbolic operators for task and motion planning
Tom Silver, Rohan Chitnis, Joshua Tenenbaum, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2021
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Human-level reinforcement learning through theory-based modeling, exploration, and planning, 2021
Pedro A. Tsividis, Joao Loula, Jake Burga, Nathan Foss, Andres Campero, Thomas Pouncy, Samuel J. Gershman, and Joshua B. Tenenbaum · 2021
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Recurrent independent mechanisms
Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, and Bernhard Schölkopf · 2021
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Symbolic metaprogram search improves learning efficiency and explains rule learning in humans
Joshua S Rule, Steven T Piantadosi, Andrew Cropper, Kevin Ellis, Maxwell Nye, and Joshua B Tenenbaum · 2024
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Diffusion for world modeling: Visual details matter in atari
Eloi Alonso, Adam Jelley, Vincent Micheli, Anssi Kanervisto, Amos Storkey, Tim Pearce, and François Fleuret · 2024
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Diffusion model predictive control
Guangyao Zhou, Sivaramakrishnan Swaminathan, Rajkumar Vasudeva Raju, J Swaroop Guntupalli, Wolfgang Lehrach, Joseph Ortiz, Antoine Dedieu, Miguel Lázaro-Gredilla, and Kevin Murphy · 2024
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Generating code world models with large language models guided by monte carlo tree search
Nicola Dainese, Matteo Merler, Minttu Alakuijala, and Pekka Marttinen · 2024
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Code repair with llms gives an exploration-exploitation tradeoff
Hao Tang, Keya Hu, Jin Zhou, Si Cheng Zhong, Wei-Long Zheng, Xujie Si, and Kevin Ellis · 2024
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Visualpredicator: Learning abstract world models with neuro-symbolic predicates for robot planning
Yichao Liang, Nishanth Kumar, Hao Tang, Adrian Weller, Joshua B Tenenbaum, Tom Silver, João F Henriques, and Kevin Ellis · 2024
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Factorsim: Generative simulation via factorized representation
Fan-Yun Sun, SI Harini, Angela Yi, Yihan Zhou, Alex Zook, Jonathan Tremblay, Logan Cross, Jiajun Wu, and Nick Haber · 2024
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Bootstrapping cognitive agents with a large language model
Feiyu Zhu and Reid Simmons · 2024
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Partially observable task and motion planning with uncertainty and risk awareness
Aidan Curtis, George Matheos, Nishad Gothoskar, Vikash Mansinghka, Joshua Tenenbaum, Tomás Lozano-Pérez, and Leslie Pack Kaelbling · 2024
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Modeling human activity comprehension at human scale: prediction, segmentation, and categorization
Tan T Nguyen, Matthew A Bezdek, Samuel J Gershman, Aaron F Bobick, Todd S Braver, and Jeffrey M Zacks · 2024
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Approximate planning in spatial search
Marta Kryven, Suhyoun Yu, Max Kleiman-Weiner, Tomer Ullman, and Joshua Tenenbaum · 2024
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Hypothesis search: Inductive reasoning with language models
Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber, and Noah D Goodman · 2024
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Doing experiments and revising rules with natural language and probabilistic reasoning
Top Piriyakulkij, Cassidy Langenfeld, Tuan Anh Le, and Kevin Ellis · 2024
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Overcoming the expressivity-efficiency tradeoff in program induction
Samuel Acquaviva · 2024
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Genie: Generative interactive environments
Jake Bruce, Michael D Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, et al · 2024
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RoboDreamer: Learning compositional world models for robot imagination
Siyuan Zhou, Yilun Du, Jiaben Chen, Yandong Li, Dit-Yan Yeung, and Chuang Gan · 2024
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Cognitive neuroscience. the biology of the mind. sixth edition, 2025
Michael S Gazzaniga, Richard B Ivry, and GR Mangun · 2025
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Synthesizing world models for bilevel planning
Zergham Ahmed, Joshua B Tenenbaum, Christopher J Bates, and Samuel J Gershman · 2025
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Deep reinforcement learning via object-centric attention
Jannis Blüml, Cedric Derstroff, Bjarne Gregori, Elisabeth Dillies, Quentin Delfosse, and Kristian Kersting · 2025
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Cognitive maps are generative programs
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LMAct: A benchmark for in-context imitation learning with long multimodal demonstrations
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