FFNSL: Feed-Forward Neural-Symbolic Learner
Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified in a structured logical form. To address this limitation, we propose a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FFN...
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| Published in: | Machine learning Vol. 112; no. 2; pp. 515 - 569 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Springer US
01.02.2023
Springer Nature B.V |
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| ISSN: | 0885-6125, 1573-0565 |
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| Abstract | Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified in a structured logical form. To address this limitation, we propose a neural-symbolic learning framework, called
Feed-Forward Neural-Symbolic Learner (FFNSL)
, that integrates a logic-based machine learning system capable of learning from noisy examples, with neural networks, in order to learn interpretable knowledge from labelled unstructured data. We demonstrate the generality of FFNSL on four neural-symbolic classification problems, where different pre-trained neural network models and logic-based machine learning systems are integrated to learn interpretable knowledge from sequences of images. We evaluate the robustness of our framework by using images subject to distributional shifts, for which the pre-trained neural networks may predict incorrectly and with high confidence. We analyse the impact that these shifts have on the accuracy of the learned knowledge and run-time performance, comparing FFNSL to tree-based and pure neural approaches. Our experimental results show that FFNSL outperforms the baselines by learning more accurate and interpretable knowledge with fewer examples. |
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| AbstractList | Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified in a structured logical form. To address this limitation, we propose a neural-symbolic learning framework, called
Feed-Forward Neural-Symbolic Learner (FFNSL)
, that integrates a logic-based machine learning system capable of learning from noisy examples, with neural networks, in order to learn interpretable knowledge from labelled unstructured data. We demonstrate the generality of FFNSL on four neural-symbolic classification problems, where different pre-trained neural network models and logic-based machine learning systems are integrated to learn interpretable knowledge from sequences of images. We evaluate the robustness of our framework by using images subject to distributional shifts, for which the pre-trained neural networks may predict incorrectly and with high confidence. We analyse the impact that these shifts have on the accuracy of the learned knowledge and run-time performance, comparing FFNSL to tree-based and pure neural approaches. Our experimental results show that FFNSL outperforms the baselines by learning more accurate and interpretable knowledge with fewer examples. Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified in a structured logical form. To address this limitation, we propose a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FFNSL), that integrates a logic-based machine learning system capable of learning from noisy examples, with neural networks, in order to learn interpretable knowledge from labelled unstructured data. We demonstrate the generality of FFNSL on four neural-symbolic classification problems, where different pre-trained neural network models and logic-based machine learning systems are integrated to learn interpretable knowledge from sequences of images. We evaluate the robustness of our framework by using images subject to distributional shifts, for which the pre-trained neural networks may predict incorrectly and with high confidence. We analyse the impact that these shifts have on the accuracy of the learned knowledge and run-time performance, comparing FFNSL to tree-based and pure neural approaches. Our experimental results show that FFNSL outperforms the baselines by learning more accurate and interpretable knowledge with fewer examples. |
| Author | Russo, Alessandra Lobo, Jorge Cunnington, Daniel Law, Mark |
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| Cites_doi | 10.1007/s10994-014-5471-y 10.1007/s10994-006-5833-1 10.1007/s10994-021-05946-3 10.1016/j.artint.2018.03.005 10.1613/jair.5714 10.1016/j.patcog.2020.107256 10.1007/BF03037089 10.1016/j.inffus.2021.05.008 10.1017/CBO9781139342124 10.1088/0954-898X_6_3_011 10.1145/502807.502810 10.1109/5.726791 10.1016/j.fss.2006.06.016 10.21105/joss.00786 10.1609/aaai.v34i04.5962 10.1609/aaai.v35i6.16639 10.1007/978-1-4684-2001-2_9 10.24963/ijcai.2020/243 10.24963/ijcai.2017/221 10.24963/ijcai.2021/254 10.1145/2939672.2939874 10.1609/aaai.v34i03.5678 10.1007/978-3-030-31423-1_6 10.1609/aaai.v34i04.6015 10.1109/CVPR.2009.5206537 10.1017/S1471068413000689 10.1007/978-3-540-28650-9_4 10.1007/3-540-56602-3_144 10.1109/DSAA.2018.00018 10.1007/978-1-4020-9409-5 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2023 The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Keywords | Neural-symbolic learning Inductive logic programming Distributional shift Logic-based machine learning |
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| SubjectTerms | Artificial Intelligence Computer Science Control Impact analysis Logic Machine Learning Mechatronics Natural Language Processing (NLP) Neural networks Robotics Simulation and Modeling Special issue on Learning and Reasoning Unstructured data |
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| Title | FFNSL: Feed-Forward Neural-Symbolic Learner |
| URI | https://link.springer.com/article/10.1007/s10994-022-06278-6 https://www.proquest.com/docview/2771811807 |
| Volume | 112 |
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