A hybrid approach to inference in probabilistic non-monotonic logic programming

We present a probabilistic inductive logic programming framework which integrates non-monotonic reasoning, probabilistic inference and parameter learning. In contrast to traditional approaches to probabilistic Answer Set Programming (ASP), our framework imposes only comparatively little restrictions on probabilistic logic programs - in particular, it allows for ASP as well as FOL syntax, and for precise as well as imprecise (interval valued) probabilities. User-configurable sampling and inference algorithms, which can be combined in a pipeline-like fashion, provide for general as well as specialized, more scalable approaches to uncertainty reasoning, allowing for adaptability with regard to different reasoning and learning tasks.