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Probabilistic Inductive Logic Programming

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Algorithmic Learning Theory (ALT 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3244))

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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.

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Author information

Authors and Affiliations

  1. Institute for Computer Science, Machine Learning Lab, Albert-Ludwigs-University, Georges-Köhler-Allee, Gebäude 079, D-79110, Freiburg i. Brg, Germany

    Luc De Raedt & Kristian Kersting

Authors
  1. Luc De Raedt
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  2. Kristian Kersting
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Editor information

Editors and Affiliations

  1. David R. Cheriton School of Computer Science University of Waterloo,  

    Shoham Ben-David

  2. Department of Computer & Information Sciences, University of Delaware, 103 Smith Hall, DE 19716, Newark

    John Case

  3. Dept. of Information Technology and Electronics, Ishinomaki Senshu University,  

    Akira Maruoka

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De Raedt, L., Kersting, K. (2004). Probabilistic Inductive Logic Programming. In: Ben-David, S., Case, J., Maruoka, A. (eds) Algorithmic Learning Theory. ALT 2004. Lecture Notes in Computer Science(), vol 3244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30215-5_3

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  • DOI: https://doi.org/10.1007/978-3-540-30215-5_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23356-5

  • Online ISBN: 978-3-540-30215-5

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