dc.contributor.advisor | Kaelbling, Leslie P | |
dc.contributor.author | Zeng, Catherine Yingxuan | |
dc.date.accessioned | 2022-05-26T03:57:10Z | |
dc.date.created | 2022 | |
dc.date.issued | 2022-05-23 | |
dc.date.submitted | 2022 | |
dc.identifier.citation | Zeng, Catherine Yingxuan. 2022. Machine Learning for Automated Planning. Bachelor's thesis, Harvard College. | |
dc.identifier.other | 29061385 | |
dc.identifier.uri | https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37371729 | * |
dc.description.abstract | Automated planning is a long-standing problem which concerns finding an action sequence to solve a task. In this thesis, we explore two problems in leveraging machine learning for automated planning: (1) learning from failed planning attempts to improve efficiency of future planning, and (2) adding goal-conditioning to action samplers in a neuro-symbolic planning framework. In both problems, we utilize neural networks to learn mappings that empirically enhance the performance of existing planning frameworks. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dash.license | LAA | |
dc.subject | Artificial intelligence | |
dc.title | Machine Learning for Automated Planning | |
dc.type | Thesis or Dissertation | |
dash.depositing.author | Zeng, Catherine Yingxuan | |
dc.date.available | 2022-05-26T03:57:10Z | |
thesis.degree.date | 2022 | |
thesis.degree.grantor | Harvard College | |
thesis.degree.level | Bachelor's | |
thesis.degree.level | Undergraduate | |
thesis.degree.name | AB | |
dc.contributor.committeeMember | Doshi-Velez, Finale | |
dc.contributor.committeeMember | Smith, Michael D | |
dc.type.material | text | |
thesis.degree.department | Computer Science | |
dash.author.email | czeng6@gmail.com | |