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Conference Papers Year : 2021

Reinforcement Symbolic Learning


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.
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hal-03327706 , version 1 (27-08-2021)




  • HAL Id : hal-03327706 , version 1


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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