Inductive Logic Programming: Cha

作者：Luc De Raedt and Kristian Kersting

Abstract. Probabilistic inductive logic programming, sometimes also
called statistical relational learning, addresses one of the central questions
of artificial intelligence: the integration of probabilistic reasoning
with first order logic representations and machine learning. A rich variety
of different formalisms and learning techniques have been developed. In
the present paper, we start from inductive logic programming and sketch
how it can be extended with probabilistic methods.
More precisely, we outline three classical settings for inductive logic programming,
namely learning from entailment, learning from interpretations,
and learning from proofs or traces, and show how they can be
used to learn different types of probabilistic representations.