We describe a prototypical software framework for probabilistic inductive logic programming which supports the seamless combination of non-monotonic reasoning, probabilistic inference and parameter learning. While building upon existing as well as new approaches to probabilistic Answer Set Programming, our framework distinguishes itself from related works by placing virtually no restrictions on the annotation of knowledge with probabilities. User-configurable algorithms provide for general as well as specialized, scalable approaches to inference and parameter learning, allowing for adaptability with regard to complex reasoning and weight learning tasks.
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