PrASP Report
It provides a flexible tool for researchers in AI and logic programming, though it appears incremental as it builds on existing probabilistic logic programming approaches.
The paper introduces PrASP, a probabilistic inductive logic programming framework that integrates non-monotonic reasoning, probabilistic inference, and parameter learning with minimal restrictions on logic programs, supporting ASP and First-Order Logic syntax and various probability annotations.
This technical report describes the usage, syntax, semantics and core algorithms of the probabilistic inductive logic programming framework PrASP. PrASP is a research software which integrates non-monotonic reasoning based on Answer Set Programming (ASP), probabilistic inference and parameter learning. In contrast to traditional approaches to Probabilistic (Inductive) Logic Programming, our framework imposes only little restrictions on probabilistic logic programs. In particular, PrASP allows for ASP as well as First-Order Logic syntax, and for the annotation of formulas with point probabilities as well as interval probabilities. A range of widely configurable inference algorithms can be combined in a pipeline-like fashion, in order to cover a variety of use cases.