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.