Full Description
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.
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.
