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Probabilistic inductive logic programming. (English) Zbl 1137.68530
De Raedt, Luc (ed.) et al., Probabilistic inductive logic programming. Theory and applications. Berlin: Springer (ISBN 978-3-540-78651-1/pbk). Lecture Notes in Computer Science 4911. Lecture Notes in Artificial Intelligence, 1-27 (2008).
Summary: Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far.
In this chapter, we start from inductive logic programming and sketch how the inductive logic programming formalisms, settings and techniques can be extended to the statistical case. 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 adapted to cover state-of-the-art statistical relational learning approaches.
For the entire collection see [Zbl 1132.68007].

MSC:
68T05 Learning and adaptive systems in artificial intelligence
68N17 Logic programming
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