Probabilistic Logic Network

A probabilistic logic network (PLN) is a novel conceptual, mathematical and computational approach to uncertain inference; inspired by logic programming, but using probabilities in place of crisp (true/false) truth values, and fractional uncertainty in place of crisp known/unknown values. In order to carry out effective reasoning in real-world circumstances, artificial intelligence software must robustly handle uncertainty. However, 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. Going beyond prior probabilistic approaches to uncertain inference, PLN is able to encompass within uncertain logic such ideas as induction, abduction, analogy, fuzziness and speculation, and reasoning about time and causality.

PLN was developed by Ben Goertzel, Matt Ikle, Izabela Lyon Freire Goertzel and Ari Heljakka for use as a cognitive algorithm used by MindAgents within the OpenCog Core. PLN was developed originally for use within the Novamente Cognition Engine.