A probabilistic logic network (PLN) is a conceptual, mathematical and computational approach to uncertain inference.
In order to carry out effective reasoning in real-world circumstances, artificial intelligence software handles uncertainty.
Previous approaches to uncertain inference do not have the breadth of scope required to provide an integrated treatment of the disparate forms of cognitively critical uncertainty as they manifest themselves within the various forms of pragmatic inference.
PLN represents truth values as intervals, but with different semantics than in imprecise probability theory.
The current version of PLN has been used in narrow-AI applications such as the inference of biological hypotheses from knowledge extracted from biological texts via language processing, and to assist the reinforcement learning of an embodied agent, in a simple virtual world, as it is taught to play "fetch".