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Introduction to the special issue on probability, logic and learning

  • JAMES CUSSENS (a1), LUC DE RAEDT (a2), ANGELIKA KIMMIG (a2) and TAISUKE SATO (a3)

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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.

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References

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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.
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. 24622467.
Getoor, L. and Taskar, B., Eds. 2007. Statistical Relational Learning. MIT Press, Cambridge, MA.
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, 3338.
Poole, D. 2008. The independent choice logic and beyond. Lecture Notes in Computer Science, Vol. 4911. Springer, New York, NY. (See De Raedt 2008), 222243.
Richardson, M. and Domingos, P. 2006. Markov logic networks. Machine Learning 62, 12, 107136.
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, 715729.
Sato, T. and Kameya, Y. 2001. Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research 15, 391454.
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, 431445.

Introduction to the special issue on probability, logic and learning

  • JAMES CUSSENS (a1), LUC DE RAEDT (a2), ANGELIKA KIMMIG (a2) and TAISUKE SATO (a3)

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