TY - JOUR
T1 - Inductive Logic Programming at 30
T2 - A New Introduction
AU - Cropper, Andrew
AU - Dumančić, Sebastijan
PY - 2022
Y1 - 2022
N2 - Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.
AB - Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.
UR - http://www.scopus.com/inward/record.url?scp=85136259691&partnerID=8YFLogxK
U2 - 10.1613/jair.1.13507
DO - 10.1613/jair.1.13507
M3 - Article
AN - SCOPUS:85136259691
SN - 1076-9757
VL - 74
SP - 765
EP - 850
JO - Journal of Artificial Intelligence Research
JF - Journal of Artificial Intelligence Research
ER -