Trabajo presentado a la 31th Technical Communications of the International Conference on Logic Programming celebrada en Cork (Irlanda) del 31 de agosto al 5 de septiembre de 2015. ; There have been great advances in the probabilistic planning community during recent years, and planners can now provide solutions for very complex probabilistic tasks. However, planners require to have a model that represents the dynamics of the system, and in general these models are built by hand. In this paper, we present a framework to automatically infer probabilistic models from observations of the state transitions of a dynamic system. We propose an extension of previous works that perform learning from interpretation transitions. These works consider as input a set of state transitions and build a logic program that realizes the given transition relations. Here we extend this method to learn a compact set of probabilistic planning operators that capture probabilistic dynamics. Finally, we provide experimental validation of the quality of the learned models. ; This research is supported in part by the JSPS 2014-2017 Grants-in-Aid for Scientific Research (B) No. 26540122 and 2014-2015 Challenging Exploratory Research No. 26280092. D. Martínez is also supported by the Spanish Ministry of Education, Culture and Sport via a FPU doctoral grant (FPU12-04173). ; Peer reviewed