Inductive logic programming at 30

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods,...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Machine learning Ročník 111; číslo 1; s. 147 - 172
Hlavní autori: Cropper, Andrew, Dumančić, Sebastijan, Evans, Richard, Muggleton, Stephen H.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.01.2022
Springer Nature B.V
Predmet:
Artificial Intelligence
Computer Science
Control
Logic programming
Logic programs
Machine Learning
Mechatronics
Natural Language Processing (NLP)
Robotics
S.i.: Lr 2020
Simulation and Modeling
Special issue on Learning and Reasoning
Relational learning
Inductive logic programming
Program synthesis
Program induction
ISSN:0885-6125, 1573-0565
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-021-06089-1