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Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring
Robotic agents should be able to learn from sub-symbolic sensor data and, at the same time, be able to reason about objects and communicate with humans on a symbolic level. This raises the question of how to overcome the gap between symbolic and sub-symbolic artificial intelligence. We propose a sem...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806026/ https://www.ncbi.nlm.nih.gov/pubmed/33501267 http://dx.doi.org/10.3389/frobt.2020.00100 |