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Learning action models with minimal observability

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Learning action models with minimal observability

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Aineto, D.; Jiménez-Celorrio, S.; Onaindia De La Rivaherrera, E. (2019). Learning action models with minimal observability. Artificial Intelligence. 275:104-137. https://doi.org/10.1016/j.artint.2019.05.003

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/144560

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Title: Learning action models with minimal observability
Author: Aineto, Diego Jiménez-Celorrio, Sergio Onaindia De La Rivaherrera, Eva
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Embargo end date: 2021-05-16
Abstract:
[EN] This paper presents FAMA, a novel approach for learning STRIPS action models from observations of plan executions that compiles the learning task into a classical planning task. Unlike all existing learning systems, ...[+]
Subjects: Action model learning , Al planning , Machine learning
Copyrigths: Embargado
Source:
Artificial Intelligence. (issn: 0004-3702 )
DOI: 10.1016/j.artint.2019.05.003
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.artint.2019.05.003
Project ID:
MECYD/FPU16/03184
AGENCIA ESTATAL DE INVESTIGACION/RYC-2015-18009
AEI/TIN2017-88476-C2-1-R
Thanks:
This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R. Diego Aineto is partially supported by the FPU16/03184 and Sergio Jimenez by the RYC15/18009, both programs funded by the Spanish government.
Type: Artículo

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