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...

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Bibliographic Details
Published inArtificial Neural Networks and Machine Learning - ICANN 2021 Vol. 12894; pp. 608 - 612
Main Authors Mercier, Chloé, Alexandre, Frédéric, Viéville, Thierry
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2021
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Models for learning sciences
Ontology edit distances
Reinforcement symbolic learning
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Summary: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.
Bibliography:Supported by Inria, AEx AIDE https://team.inria.fr/mnemosyne/en/aide.
ISBN:9783030863791
3030863794
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-86380-7_49