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Inductive Logic Programming

Overview of attention for book
Cover of 'Inductive Logic Programming'

Table of Contents

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    Book Overview
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    Chapter 1 A Constrained Optimization Approach to Set the Parameters of Probabilistic Answer Set Programs
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    Chapter 2 Regularization in Probabilistic Inductive Logic Programming
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    Chapter 3 Towards ILP-Based $$\text {LTL}_\text {f}$$ LTL f Passive Learning
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    Chapter 4 Learning Strategies of Inductive Logic Programming Using Reinforcement Learning
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    Chapter 5 Select First, Transfer Later: Choosing Proper Datasets for Statistical Relational Transfer Learning
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    Chapter 6 GNN Based Extraction of Minimal Unsatisfiable Subsets
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    Chapter 7 What Do Counterfactuals Say About the World? Reconstructing Probabilistic Logic Programs from Answers to “What If?” Queries
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    Chapter 8 Few-Shot Learning of Diagnostic Rules for Neurodegenerative Diseases Using Inductive Logic Programming
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    Chapter 9 An Experimental Overview of Neural-Symbolic Systems
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    Chapter 10 Statistical Relational Structure Learning with Scaled Weight Parameters
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    Chapter 11 A Review of Inductive Logic Programming Applications for Robotic Systems
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    Chapter 12 Meta-interpretive Learning from Fractal Images
Attention for Chapter 1: A Constrained Optimization Approach to Set the Parameters of Probabilistic Answer Set Programs
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Chapter title
A Constrained Optimization Approach to Set the Parameters of Probabilistic Answer Set Programs
Chapter number 1
Book title
Inductive Logic Programming
Published by
Springer, Cham, January 2023
DOI 10.1007/978-3-031-49299-0_1
Book ISBNs
978-3-03-149298-3, 978-3-03-149299-0
Authors

Azzolini, Damiano

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