| Schema networks: Zero-shot transfer with a generative causal model of intuitive physics K Kansky, T Silver, DA Mély, M Eldawy, M Lázaro-Gredilla, X Lou, ... International conference on machine learning, 1809-1818, 2017 | 246 | 2017 |
| Integrated task and motion planning CR Garrett, R Chitnis, R Holladay, B Kim, T Silver, LP Kaelbling, ... Annual review of control, robotics, and autonomous systems 4, 265-293, 2021 | 215 | 2021 |
| Residual policy learning T Silver, K Allen, J Tenenbaum, L Kaelbling arXiv preprint arXiv:1812.06298, 2018 | 120 | 2018 |
| Transforming clinical data into actionable prognosis models: machine-learning framework and field-deployable app to predict outcome of Ebola patients A Colubri, T Silver, T Fradet, K Retzepi, B Fry, P Sabeti PLoS neglected tropical diseases 10 (3), e0004549, 2016 | 68 | 2016 |
| Online bayesian goal inference for boundedly rational planning agents T Zhi-Xuan, J Mann, T Silver, J Tenenbaum, V Mansinghka Advances in neural information processing systems 33, 19238-19250, 2020 | 51 | 2020 |
| Planning with learned object importance in large problem instances using graph neural networks T Silver, R Chitnis, A Curtis, JB Tenenbaum, T Lozano-Pérez, ... Proceedings of the AAAI conference on artificial intelligence 35 (13), 11962 …, 2021 | 50 | 2021 |
| PDDLGym: Gym environments from PDDL problems T Silver, R Chitnis arXiv preprint arXiv:2002.06432, 2020 | 41 | 2020 |
| Learning symbolic operators for task and motion planning T Silver, R Chitnis, J Tenenbaum, LP Kaelbling, T Lozano-Pérez 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 31 | 2021 |
| Few-Shot Bayesian Imitation Learning with Logical Program Policies T Silver, KR Allen, AK Lew, L Kaelbling, J Tenenbaum Thirty-Fourth AAAI Conference on Artificial Intelligence, 0 | 30* | |
| Behavior is everything: Towards representing concepts with sensorimotor contingencies N Hay, M Stark, A Schlegel, C Wendelken, D Park, E Purdy, T Silver, ... Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 24 | 2018 |
| Learning sparse relational transition models V Xia, W Zi, K Allen, T Silver, LP Kaelbling International Conference on Learning Representations (ICLR), 2019 | 23 | 2019 |
| Glib: Efficient exploration for relational model-based reinforcement learning via goal-literal babbling R Chitnis, T Silver, JB Tenenbaum, LP Kaelbling, T Lozano-Pérez Proceedings of the AAAI Conference on Artificial Intelligence 35 (13), 11782 …, 2021 | 22* | 2021 |
| CAMPS: Learning context-specific abstractions for efficient planning in factored MDPs R Chitnis, T Silver, B Kim, L Kaelbling, T Lozano-Perez Conference on Robot Learning, 64-79, 2021 | 21 | 2021 |
| Learning neuro-symbolic relational transition models for bilevel planning R Chitnis, T Silver, JB Tenenbaum, T Lozano-Perez, LP Kaelbling 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022 | 20 | 2022 |
| Reinforcement learning for classical planning: Viewing heuristics as dense reward generators C Gehring, M Asai, R Chitnis, T Silver, L Kaelbling, S Sohrabi, M Katz Proceedings of the International Conference on Automated Planning and …, 2022 | 16 | 2022 |
| Learning neuro-symbolic skills for bilevel planning T Silver, A Athalye, JB Tenenbaum, T Lozano-Perez, LP Kaelbling arXiv preprint arXiv:2206.10680, 2022 | 14 | 2022 |
| Inventing relational state and action abstractions for effective and efficient bilevel planning T Silver, R Chitnis, N Kumar, W McClinton, T Lozano-Perez, LP Kaelbling, ... arXiv preprint arXiv:2203.09634, 2022 | 13 | 2022 |
| Discovering state and action abstractions for generalized task and motion planning A Curtis, T Silver, JB Tenenbaum, T Lozano-Pérez, L Kaelbling Proceedings of the AAAI Conference on Artificial Intelligence 36 (5), 5377-5384, 2022 | 12 | 2022 |
| Learning constraint-based planning models from demonstrations J Loula, K Allen, T Silver, J Tenenbaum 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 10 | 2020 |
| PDDL Planning with Pretrained Large Language Models T Silver, V Hariprasad, RS Shuttleworth, N Kumar, T Lozano-Pérez, ... NeurIPS 2022 Foundation Models for Decision Making Workshop, 0 | 9 | |
