Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring

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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Bibliographic Details
Main Authors: Pedro Zuidberg Dos Martires, Nitesh Kumar, Andreas Persson, Amy Loutfi, Luc De Raedt
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-07-01
Series:Frontiers in Robotics and AI
Subjects:
semantic world modeling
perceptual anchoring
probabilistic anchoring
statistical relational learning
probabilistic logic programming
object tracking
Online Access:https://www.frontiersin.org/article/10.3389/frobt.2020.00100/full

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https://www.frontiersin.org/article/10.3389/frobt.2020.00100/full

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