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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Vydáno v:	Machine learning Ročník 111; číslo 1; s. 123 - 145
Hlavní autoři:	Gao, Kun, Wang, Hanpin, Cao, Yongzhi, Inoue, Katsumi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.01.2022 Springer Nature B.V
Témata:	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
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Abstract	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. In this paper, we propose a novel differentiable inductive logic programming system called differentiable learning from interpretation transition (D-LFIT) for learning logic programs through the proposed embeddings of logic programs, neural networks, optimization algorithms, and an adapted algebraic method to compute the logic program semantics. The proposed model has several characteristics, including a small number of parameters, the ability to generate logic programs in a curriculum-learning setting, and linear time complexity for the extraction of trained neural networks. The well-known bottom clause positionalization algorithm is incorporated when the proposed system learns from relational datasets. We compare our model with NN-LFIT, which extracts propositional logic rules from retuned connected networks, the highly accurate rule learner RIPPER, the purely symbolic LFIT system LF1T, and CILP++, which integrates neural networks and the propositionalization method to handle first-order logic knowledge. From the experimental results, we conclude that D-LFIT yields comparable accuracy with respect to the baselines when given complete, incomplete, and mislabeled data. Our experimental results indicate that D-LFIT not only learns symbolic logic programs quickly and precisely but also performs robustly when processing mislabeled and incomplete datasets.
AbstractList	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. In this paper, we propose a novel differentiable inductive logic programming system called differentiable learning from interpretation transition (D-LFIT) for learning logic programs through the proposed embeddings of logic programs, neural networks, optimization algorithms, and an adapted algebraic method to compute the logic program semantics. The proposed model has several characteristics, including a small number of parameters, the ability to generate logic programs in a curriculum-learning setting, and linear time complexity for the extraction of trained neural networks. The well-known bottom clause positionalization algorithm is incorporated when the proposed system learns from relational datasets. We compare our model with NN-LFIT, which extracts propositional logic rules from retuned connected networks, the highly accurate rule learner RIPPER, the purely symbolic LFIT system LF1T, and CILP++, which integrates neural networks and the propositionalization method to handle first-order logic knowledge. From the experimental results, we conclude that D-LFIT yields comparable accuracy with respect to the baselines when given complete, incomplete, and mislabeled data. Our experimental results indicate that D-LFIT not only learns symbolic logic programs quickly and precisely but also performs robustly when processing mislabeled and incomplete datasets.
Author	Inoue, Katsumi Cao, Yongzhi Gao, Kun Wang, Hanpin
Author_xml	– sequence: 1 givenname: Kun surname: Gao fullname: Gao, Kun organization: Peking University – sequence: 2 givenname: Hanpin surname: Wang fullname: Wang, Hanpin email: whpxhy@pku.edu.cn organization: Peking University, Guangzhou University – sequence: 3 givenname: Yongzhi surname: Cao fullname: Cao, Yongzhi organization: Peking University – sequence: 4 givenname: Katsumi surname: Inoue fullname: Inoue, Katsumi organization: National Institute of Informatics
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Snippet	The combination of learning and reasoning is an essential and challenging topic in neuro-symbolic research. Differentiable inductive logic programming is a...
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SubjectTerms	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
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Title	Learning from interpretation transition using differentiable logic programming semantics
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