Inductive Logic Programming
Springer International Publishing (Verlag)
978-3-031-55629-6 (ISBN)
This book constitutes the refereed proceedings of the 31st International Conference on Inductive Logic Programming, ILP 2022, held during September 28-30, 2022.
The 11 regular papers presented in this book were carefully reviewed and selected from 26 submissions
The papers in these proceedings represent the diversity and vitality in present ILP research, including statistical relational learning, transfer learning, scientific reasoning, learning temporal models, synthesis and planning, and argumentation and language.
Learning the Parameters of Probabilistic Answer Set Programs.- Navigable atom-rule interactions in PSL models enhanced by rule verbalizations, with an application to etymological inference.- A Program-Synthesis Challenge for ARC-like Tasks.- Explaining with Attribute-based and Relational Near Misses: An Interpretable Approach to Distinguishing Facial Expressions of Pain and Disgust.- Learning Automata-Based Complex Event Patterns in Answer Set Programming.- Learning Hierarchical Problem Networks for Knowledge-Based Planning.- Combining word embeddings-based similarity measures for transfer learning across relational domains.- Learning Assumption-based Argumentation Frameworks.- Diagnosis of Event Sequences with LFIT.- Efficient Abductive Learning of Microbial Interactions using Meta Inverse Entailment.- Functional Lifted Bayesian Networks: Statistical Relational Learning and Reasoning with Relative Frequencies.
| Erscheinungsdatum | 22.03.2024 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | X, 157 p. 35 illus., 20 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | answer set programming • Argumentation • Automated scientific discovery • inductive logic programming (ilp) • logical and relational learning • Logic Programming • Meta-interpretative learning • multi-relational data mining • statistical relational learning |
| ISBN-10 | 3-031-55629-1 / 3031556291 |
| ISBN-13 | 978-3-031-55629-6 / 9783031556296 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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