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Symbolic knowledge extraction from trained neural networks: A sound approach
Artificial Intelligence 125 (1-2):155-207 (2001)
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Citations of this work
A differentiable first-order rule learner for inductive logic programming.Kun Gao, Katsumi Inoue, Yongzhi Cao & Hanpin Wang - 2024 - Artificial Intelligence 331 (C):104108.
Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples.Tarek R. Besold, Artur D’Avila Garcez, Keith Stenning, Leendert van der Torre & Michiel van Lambalgen - 2017 - Minds and Machines 27 (1):37-77.
Using ontologies to enhance human understandability of global post-hoc explanations of black-box models.Roberto Confalonieri, Tillman Weyde, Tarek R. Besold & Fermín Moscoso del Prado Martín - 2021 - Artificial Intelligence 296 (C):103471.
Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples.Henri Prade, Markus Knauff, Igor Douven & Gabriele Kern-Isberner - 2017 - Minds and Machines 27 (1):37-77.
References found in this work
Argument-based extended logic programming with defeasible priorities.Henry Prakken & Giovanni Sartor - 1997 - Journal of Applied Non-Classical Logics 7 (1-2):25-75.
Knowledge-based artificial neural networks.Geoffrey G. Towell & Jude W. Shavlik - 1994 - Artificial Intelligence 70 (1-2):119-165.
Horn approximations of empirical data.Henry Kautz, Michael Kearns & Bart Selman - 1995 - Artificial Intelligence 74 (1):129-145.
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