Learning from interpretation transition using differentiable logic programming semantics

The combination of learning and reasoning is an essential and challenging topic in neuro-symbolic research. Differentiable inductive logic programming is a technique for learning a symbolic knowledge representation from either complete, mislabeled, or incomplete observed facts using neural networks....

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Bibliographic Details
Published in:	Machine learning Vol. 111; no. 1; pp. 123 - 145
Main Authors:	Gao, Kun, Wang, Hanpin, Cao, Yongzhi, Inoue, Katsumi
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.01.2022 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Computer Science Control Curricula Datasets Knowledge representation Learning Logic programming Logic programs Machine Learning Mechatronics Natural Language Processing (NLP) Neural networks Optimization Robotics Semantics Simulation and Modeling Special issue on Learning and Reasoning Neuro-symbolic method Differentiable inductive logic programming Explainability Learning from interpretation transition Machine learning
ISSN:	0885-6125, 1573-0565
Online Access:	Get full text
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