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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Vydáno v:Machine learning Ročník 112; číslo 2; s. 515 - 569
Hlavní autoři: Cunnington, Daniel, Law, Mark, Lobo, Jorge, Russo, Alessandra
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.02.2023
Springer Nature B.V
Témata:
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
Neural-symbolic learning
Inductive logic programming
Distributional shift
Logic-based machine learning
ISSN:0885-6125, 1573-0565
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Shrnutí: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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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-022-06278-6