Applications of Probabilistic Logic Programming

Probabilistic logic programs combine the power of a programming language with a possible world semantics; they are typically based on Sato's distribution semantics [8] and they have been studied for over twenty years now. In this talk, I shall report on recent progress in applying this paradigm to challenging applications. The first application domain will be that of robotics, where we have developed extensions of the basic distribution semantics to cope with dynamics as well continuous distributions [2]. The resulting representations are now being used to learn multi-relational object affordances, which specify the conditions under which actions can be applied on particular objects [3,4]. The second application is in a biological domain, where a decision theoretic extension of the distribution semantics [1] is the underlying inference engine of the PheNetic system [5,6], which extracts from an interactome, the sub-network that best explains genes prioritized through a molecular profiling experiment. Finally, I shall report on our results in applying ProbFOIL [7] to the problem of machine reading in CMU's Never Ending Language Learning system. ProbFOIL is an extension of the traditional rule-learning system FOIL for use with the distribution semantics. [1] G. Van den Broeck, I. Thon, M. van Otterlo, L. De Raedt. DTProbLog: A decision-theoretic probabilistic Prolog. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI 2010. [2] B. Gutmann, I. Thon, A. Kimmig, M. Bruynooghe, and L. De Raedt. The magic of logical inference in probabilistic programming. Theory and Practice of Logic Programming, 2011(11), pages 663-680. [3] Nitti, D., De Laet, T., De Raedt, L. (2013). A particle filter for hybrid relational domains. in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS 2013 (pp. 2764-2771). [4] Moldovan, B., Moreno, P., van Otterlo, M., Santos-Victor, J., De Raedt, L. (2012). Learning relational affordance models for robots in multi-object manipulation tasks. In Proc. IEEE International Conference on Robotics and Automation, ICRA 2012. 2012 (pp. 4373 -4378). [5] De Maeyer D, Weytjens B, Renkens J, De Raedt L, Marchal K. PheNetic: network-based interpretation of molecular profiling data. Nucleic Acids Res. 2015. [6] De Maeyer D, Renkens J, Cloots L, De Raedt L, Marchal K. PheNetic: network-based interpretation of unstructured gene lists in E. coli. Mol Biosyst. 2013 Jul;9(7):1594-603. [7] De Raedt, L. Dries, A., Thon, I., Van den Broeck, G., Verbeke, M. Inducing Probabilistic Relational Rules from Probabilistic Examples, In Proc. International Joint Conference on AI, IJCAI 2015, in press. [8] Sato, T., A Statistical Learning Method for Logic Programs with Distribution Semantics, In Proc. 12th International Conference on Logic Programming, ICLP 1995, pp. 715--729.Invited talk.status: publishe