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...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2020-07-01
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Series: | Frontiers in Robotics and AI |
Subjects: | |
Online Access: | https://www.frontiersin.org/article/10.3389/frobt.2020.00100/full |