Inductive Logic Programming
Springer International Publishing (Verlag)
978-3-031-49298-3 (ISBN)
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.
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.
| Erscheinungsdatum | 23.12.2023 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | XVIII, 175 p. 40 illus., 35 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 305 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | Counterfactual reasoning • graph neural network • linear temporal logic • Meta-Interpretive Learning • meta learning • Neural-Symbolic AI • (Probabilistic) Answer Set Programming • probabilistic logic programming • Robotics, Assumption-Based Argumentation • transfer learning |
| ISBN-10 | 3-031-49298-6 / 3031492986 |
| ISBN-13 | 978-3-031-49298-3 / 9783031492983 |
| Zustand | Neuware |
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