Learning from interpretation transition using differentiable logic programming semantics

DOI10.1007/s10994-021-06058-8OpenAlexW3199644277MaRDI QIDQ2127221

Kun Gao, Hanpin Wang, Yongzhi Cao, Katsumi Inoue

Publication date: 20 April 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-021-06058-8

zbMATH Keywords

machine learningexplainabilitydifferentiable inductive logic programminglearning from interpretation transitionneuro-symbolic method

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Differentiable learning of matricized DNFs and its application to Boolean networks

Uses Software

• C4.5
• RelNN

Cites Work

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• RelNN
• Logic programs and connectionist networks
• The lattice structure and refinement operators for the hypothesis space bounded by a bottom clause
• Making sense of sensory input
• Learning from interpretation transition
• Oscillating Behavior of Logic Programs
• The Semantics of Predicate Logic as a Programming Language
• Inductive Logic Programming: Theory and methods
• Learning Explanatory Rules from Noisy Data
• Learning and Reasoning with Logic Tensor Networks
• Inductive logic programming
• Symbolic knowledge extraction from trained neural networks: A sound approach

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