Reinforcement Symbolic Learning
Overview & Synopsis
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.
Indexed from repository: HAL Open Science
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hal.science)
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Document Information
| Document Title | Reinforcement Symbolic Learning |
|---|---|
| Author / Organization | Chloé Mercier, Frédéric Alexandre, Thierry Viéville |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2021 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-100533 |
| Original Source Link | Visit Source Page on hal.science » |