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Abstract:

Probabilistic logic programs combine the power of a programming language with a possible world semantics; they are typically based on Sato's distribution semantics [5] and they have been studied for over twenty years now. They have recently been extended towards defining continuous distributions and dynamics, which enables their use in robotics and perception [1]. The talk shall briefly introduce these formalisms and then present some progress on synthesising such probabilistic programs from examples, both in the discrete and the continuous case. For the discrete case, I shall report on our results in applying ProbFOIL [5] 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. For the continuous case, I shall present our ongoing work in learning affordances in robotics, where the goal is to learn the conditions under which actions can be applied on particular objects [2,3 ]. [1] 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. [2] 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). [3] 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). [4] 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. [5] 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