Reinforcement Symbolic Learning
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.
Fichier principal
ICANN21_paper.pdf (206.7 Ko)
Télécharger le fichier
ICANN21_poster.pdf (836.24 Ko)
Télécharger le fichier
| Origin | Files produced by the author(s) |
|---|
