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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...
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| 出版年: | Front Robot AI |
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| 主要な著者: | , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Frontiers Media S.A.
2020
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7806026/ https://ncbi.nlm.nih.gov/pubmed/33501267 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/frobt.2020.00100 |
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