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Introduction to the special issue on probability, logic and learning

Published online by Cambridge University Press:  23 May 2014

JAMES CUSSENS,
LUC DE RAEDT,
ANGELIKA KIMMIG and
TAISUKE SATO
JAMES CUSSENS
Affiliation:
Department of Computer Science and York Centre for Complex Systems AnalysisUniversity of York, York YO10 5GE, UK (e-mail: james.cussens@york.ac.uk)
LUC DE RAEDT
Affiliation:
Department of Computer Science, KU Leuven, Celestijnenlaan 200a, 3001 Heverlee, Belgium (e-mail: luc.deraedt@cs.kuleuven.be, angelika.kimmig@cs.kuleuven.be)
ANGELIKA KIMMIG
Affiliation:
Department of Computer Science, KU Leuven, Celestijnenlaan 200a, 3001 Heverlee, Belgium (e-mail: luc.deraedt@cs.kuleuven.be, angelika.kimmig@cs.kuleuven.be)
TAISUKE SATO
Affiliation:
Department of Computer Science, Tokyo Institute of TechnologyOokayama 2-12-1, Meguro-ku, Tokyo, Japan (e-mail: sato@mi.cs.titech.ac.jp)
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Recently, the combination of probability, logic and learning has received considerable attention in the artificial intelligence and machine learning communities; see e.g. Getoor and Taskar (2007); De Raedt et al. (2008). Computational logic often plays a major role in these developments since it forms the theoretical backbone for much of the work in probabilistic programming and logical and relational learning. Contemporary work in this area is often application- and experiment-driven, but is also concerned with the theoretical foundations of formalisms and inference procedures and with advanced implementation technology that scales well.

Information

Type
Introduction
Information
Theory and Practice of Logic Programming , Volume 15 , Issue 2: Probability, Logic and Learning , March 2015, pp. 145 - 146
Copyright
Copyright © Cambridge University Press 2014 

References

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