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Reinforcement Symbolic Learning

Chloé Mercier 1 Frédéric Alexandre 1 Thierry Viéville 1, 2 
1 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
2 LINE - Laboratoire d'Innovation et Numérique pour l'Education
Abstract : Complex problem solving involves representing structured knowledge, reasoning and learning, all at once. In this prospective study, we make explicit how a reinforcement learning paradigm can be applied to a symbolic representation of a concrete problem-solving task, modeled here by an ontology. This preliminary paper is only a set of ideas while feasibility verification is still a perspective of this work.
Keywords : Reinforcement Symbolic Learning Ontology Edit Distances Models for Learning Sciences
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Conference papers
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https://hal.inria.fr/hal-03327706
Contributor : Thierry Viéville Connect in order to contact the contributor
Submitted on : Friday, August 27, 2021 - 2:10:58 PM
Last modification on : Friday, August 5, 2022 - 3:51:00 AM
Long-term archiving on: : Sunday, November 28, 2021 - 6:34:01 PM

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  • HAL Id : hal-03327706, version 1

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Chloé Mercier, Frédéric Alexandre, Thierry Viéville. Reinforcement Symbolic Learning. ICANN 2021 - 30th International Conference on Artificial Neural Networks, Sep 2021, Bratislava / Virtual, Slovakia. ⟨hal-03327706⟩

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