| Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary M Asai, A Fukunaga AAAI, 6094-6101, 2018 | 213 | 2018 |
| Unsupervised Grounding of Plannable First-Order Logic Representation from Images M Asai Twenty-Ninth International Conference on Automated Planning and Scheduling …, 2019 | 69 | 2019 |
| Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS) M Asai, C Muise IJCAI, 2020 | 64 | 2020 |
| 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 | 39 | 2022 |
| Tie-breaking strategies for cost-optimal best first search M Asai, A Fukunaga Journal of Artificial Intelligence Research 58, 67-121, 2017 | 28 | 2017 |
| Tiebreaking strategies for A* search: How to explore the final frontier M Asai, A Fukunaga AAAI, 673--679, 2016 | 26* | 2016 |
| Solving large-scale planning problems by decomposition and macro generation M Asai, A Fukunaga Proceedings of the International Conference on Automated Planning and …, 2015 | 25 | 2015 |
| Classical planning in deep latent space M Asai, H Kajino, A Fukunaga, C Muise Journal of Artificial Intelligence Research 74, 1599-1686, 2022 | 20 | 2022 |
| Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder M Asai, H Kajino Twenty-Ninth International Conference on Automated Planning and Scheduling …, 2019 | 19 | 2019 |
| Exploration among and within plateaus in greedy best-first search M Asai, A Fukunaga Proceedings of the International Conference on Automated Planning and …, 2017 | 16 | 2017 |
| Photo-realistic blocksworld dataset M Asai arXiv preprint arXiv:1812.01818, 2018 | 13 | 2018 |
| Fully automated cyclic planning for large-scale manufacturing domains M Asai, A Fukunaga Proceedings of the International Conference on Automated Planning and …, 2014 | 13 | 2014 |
| Unsuccessful Neural-Symbolic Descriptive Action Model from Images: The Search for STRIPS M Asai ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS), 2020 | 6* | 2020 |
| Discrete word embedding for logical natural language understanding M Asai, Z Tang arXiv preprint arXiv:2008.11649, 2020 | 4 | 2020 |
| Scale-Adaptive Balancing of Exploration and Exploitation in Classical Planning S Wissow, M Asai arXiv preprint arXiv:2305.09840, 2023 | 2 | 2023 |
| Discovering higher-level actions from expert's action demonstration M Tatsubori, RE Fall III, DJR Agravante, M Asai, A Munawar US Patent 11,526,729, 2022 | 2 | 2022 |
| Symbolic model training with active learning A Kishimoto, M Asai, HOU Yufang, H Kajino, R Marinescu US Patent App. 17/132,776, 2022 | 1 | 2022 |
| Is Policy Learning Overrated?: Width-Based Planning and Active Learning for Atari B Ayton, M Asai Proceedings of the International Conference on Automated Planning and …, 2022 | 1 | 2022 |
| Discrete feature representation with class priority M Asai US Patent 11,244,227, 2022 | 1 | 2022 |
| Permutation-invariant optimization metrics for neural networks M Asai US Patent App. 16/366,678, 2020 | 1 | 2020 |
Alex FukunagaThe University of Tokyo在 idea.c.u-tokyo.ac.jp 的电子邮件经过验证
