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...

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Detalles Bibliográficos
Autores principales: Zuidberg Dos Martires, Pedro, Kumar, Nitesh, Persson, Andreas, Loutfi, Amy, De Raedt, Luc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Robotics and AI
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

Internet

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

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