Inductive Logic Programming: 32nd International Conference, ILP 2023, Bari, Italy, November 13-15, 2023, Proceedings

by Elena Bellodi (Editor), Francesca Alessandra Lisi (Editor), Riccardo Zese (Editor)

Overview

This book constitutes the refereed proceedings of the 32nd International Conference on Inductive Logic Programming, ILP 2023, held in Bari, Italy, during November 13–15, 2023.

The 11 full papers and 1 short paper included in this book were carefully reviewed and selected from 18 submissions. They cover all aspects of learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring intersections to statistical learning and other probabilistic approaches.

Product Details

ISBN-13:	9783031492983
Publisher:	Springer Nature Switzerland
Publication date:	12/21/2023
Series:	Lecture Notes in Computer Science , #14363
Edition description:	1st ed. 2023
Pages:	175
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

Declarative Sequential Pattern Mining in ASP.- Extracting Rules from ML models in Angluin’s Style.- A Constrained Optimization Approach to Set the Parameters of Probabilistic Answer Set Programs.- Regularization in Probabilistic Inductive Logic Programming.- Towards ILP-based LTLf passive learning.- Learning Strategies of Inductive Logic Programming Using Reinforcement Learning.- Select first, transfer later: choosing proper datasets for statistical relational transfer learning.- GNN based Extraction of Minimal Unsatisfiable Subsets.- What Do Counterfactuals Say about the World? Reconstructing Probabilistic Logic Programs from Answers to “What if?” Queries.- Few-shot learning of diagnostic rules for neurodegenerative diseases using Inductive Logic Programming.- An Experimental Overview of Neural-Symbolic Systems.- Statistical relational structure learning with scaled weight parameters.- A Review of Inductive Logic Programming Applications for Robotic Systems.- Meta Interpretive Learning from Fractal images.