TY  - BOOK
AU  - Bellodi,Elena
AU  - Lisi,Francesca Alessandra
AU  - Zese,Riccardo
TI  - Inductive Logic Programming: 32nd International Conference, ILP 2023, Bari, Italy, November 13-15, 2023, Proceedings 
T2  - Lecture Notes in Artificial Intelligence,
SN  - 9783031492990
U1  - 006.3 23
PY  - 2023///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Artificial intelligence
KW  - Computer engineering
KW  - Computer networks 
KW  - Compilers (Computer programs)
KW  - Computer science
KW  - Machine theory
KW  - Artificial Intelligence
KW  - Computer Engineering and Networks
KW  - Compilers and Interpreters
KW  - Computer Science Logic and Foundations of Programming
KW  - Formal Languages and Automata Theory
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-3-031-49299-0
ER  -