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dc.contributor.advisorKaelbling, Leslie P
dc.contributor.authorZeng, Catherine Yingxuan
dc.date.accessioned2022-05-26T03:57:10Z
dc.date.created2022
dc.date.issued2022-05-23
dc.date.submitted2022
dc.identifier.citationZeng, Catherine Yingxuan. 2022. Machine Learning for Automated Planning. Bachelor's thesis, Harvard College.
dc.identifier.other29061385
dc.identifier.urihttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37371729*
dc.description.abstractAutomated 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.mimetypeapplication/pdf
dc.language.isoen
dash.licenseLAA
dc.subjectArtificial intelligence
dc.titleMachine Learning for Automated Planning
dc.typeThesis or Dissertation
dash.depositing.authorZeng, Catherine Yingxuan
dc.date.available2022-05-26T03:57:10Z
thesis.degree.date2022
thesis.degree.grantorHarvard College
thesis.degree.levelBachelor's
thesis.degree.levelUndergraduate
thesis.degree.nameAB
dc.contributor.committeeMemberDoshi-Velez, Finale
dc.contributor.committeeMemberSmith, Michael D
dc.type.materialtext
thesis.degree.departmentComputer Science
dash.author.emailczeng6@gmail.com


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