Action Model Learning based on Grammar Induction. Grand, M. In Proceedings of the 20th ICAPS Doctoral Consortium (ICAPS DC 2022), pages 4–8, 2022. ![Action Model Learning based on Grammar Induction [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper ![Action Model Learning based on Grammar Induction [link]](https://bibbase.org/img/filetypes/link.svg "link")
Presentation abstract bibtex 8 downloads This paper presents a novel approach to learn PDDL domain called AMLSI (Action Model Learning with State machine Interaction) based on grammar induction. AMLSI learns with no prior knowledge from a training dataset made up of action sequences built by random walks and by observing state transitions. The domain learnt is accurate enough to be used without human proofreading in a planner even with very highly partial and noisy observations. Thus AMLSI takles a key issue for domain learning that is the ability to plan with the learned domains. It often happens that small learning errors lead to domains that are unusable for planning. AMLSI contribution is to learn domains from partial and noisy observations with sufficient accuracy to allow planners to solve new problems. Also, this paper presents an incremental and a temporal extension.
@InProceedings{ICAPSDC2022paper-02,
author = {Maxence Grand},
title = {Action Model Learning based on Grammar Induction},
booktitle = {Proceedings of the 20th ICAPS Doctoral Consortium (ICAPS DC 2022)},
year = {2022},
pages = {4--8},
abstract = {This paper presents a novel approach to learn PDDL domain called AMLSI (Action Model Learning with State machine Interaction) based on grammar induction. AMLSI learns with no prior knowledge from a training dataset made up of action sequences built by random walks and by observing state transitions. The domain learnt is accurate enough to be used without human proofreading in a planner even with very highly partial and noisy observations. Thus AMLSI takles a key issue for domain learning that is the ability to plan with the learned domains. It often happens that small learning errors lead to domains that are unusable for planning. AMLSI contribution is to learn domains from partial and noisy observations with sufficient accuracy to allow planners to solve new problems. Also, this paper presents an incremental and a temporal extension.},
url_paper = {https://icaps22.icaps-conference.org/dc/ICAPS_2022_paper_356.pdf},
url_presentation = {https://www.youtube.com/watch?v=TbHLkyoZ2sQ&list=PLj-ZdQ5rfSEqD1SztBXJppdjIE9CQQfzV&index=5}
}
Downloads: 8
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