Probabilistic logic programs [4] combine the power of a pro- gramming language with a possible world semantics; they are typically based on Sato’s distribution semantics [9, 8], and it is possible to learn their parameters and to some extent also their structure. They have been studied for over twenty years now. In this talk, I shall introduce the state of the art in probabilistic logic programs and report on some 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 [5]. 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 [6, 7]. The second application is in a biological domain, where a decision theoretic extension of the distribution semantics [10] is the underlying inference engine of the PheNetic system [2], which extracts from an in- teractome, the sub-network that best explains genes prioritized through a molecular profiling experiment. Finally, I shall report on our results in applying ProbFOIL [3] to the problem of machine reading in CMU’s Never Ending Language Learning system [1]. ProbFOIL is an extension of the traditional rule-learning system FOIL for use with the distribution semantics.