Reinforcement Symbolic Learning
Résumé
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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ICANN21_paper.pdf (206.7 Ko)
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ICANN21_poster.pdf (836.24 Ko)
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