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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: [email protected])
LUC DE RAEDT
Affiliation:
Department of Computer Science, KU Leuven, Celestijnenlaan 200a, 3001 Heverlee, Belgium (e-mail: [email protected], [email protected])
ANGELIKA KIMMIG
Affiliation:
Department of Computer Science, KU Leuven, Celestijnenlaan 200a, 3001 Heverlee, Belgium (e-mail: [email protected], [email protected])
TAISUKE SATO
Affiliation:
Department of Computer Science, Tokyo Institute of TechnologyOokayama 2-12-1, Meguro-ku, Tokyo, Japan (e-mail: [email protected])
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Extract

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.

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 

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References

De Raedt, L., Frasconi, P., Kersting, K. and Muggleton, S., Eds. 2008. Probabilistic Inductive Logic Programming – Theory and Applications. Lecture Notes in Computer Science, vol. 4911. Springer, Berlin, Germany.Google Scholar
De Raedt, L., Kimmig, A. and Toivonen, H. 2007. ProbLog: A probabilistic Prolog and its application in link discovery. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India, Veloso, M. M., Ed. 2462–2467.Google Scholar
Getoor, L. and Taskar, B., Eds. 2007. Statistical Relational Learning. MIT Press, Cambridge, MA.Google Scholar
Kersting, K. 2012. Lifted probabilistic inference. In Proceedings of the 20th European Conference on Artificial Intelligence (ECAI-12), De Raedt, L., Bessière, C., Dubois, D., Doherty, P., Frasconi, P., Heintz, F. and Lucas, P. J. F., Eds. Frontiers in Artificial Intelligence and Applications, vol. 242. IOS Press, Amsterdam, the Netherlands, 33–38.Google Scholar
Poole, D. 2008. The independent choice logic and beyond. Lecture Notes in Computer Science, Vol. 4911. Springer, New York, NY. (See De Raedt 2008), 222–243.Google Scholar
Richardson, M. and Domingos, P. 2006. Markov logic networks. Machine Learning 62, 1–2, 107–136.Google Scholar
Sato, T. 1995. A statistical learning method for logic programs with distribution semantics. In Proceedings of the 12th International Conference on Logic Programming (ICLP-95), Sterling, L., Ed. MIT Press, Cambridge, MA, 715–729.Google Scholar
Sato, T. and Kameya, Y. 2001. Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research 15, 391–454.Google Scholar
Vennekens, J., Verbaeten, S. and Bruynooghe, M. 2004. Logic programs with annotated disjunctions. In Proceedings of the 20th International Conference on Logic Programming (ICLP-04), Demoen, B. and Lifschitz, V., Eds. Lecture Notes in Computer Science, vol. 3132. Springer, New York, NY, 431–445.Google Scholar