Probabilistic Logic Networks

From AGIRI.org

Known also as PLN, this is the probabilistic logic developed by Ben Goertzel, Matt Ikle', Izabela Goertzel and Ari Heljakka for use within the Novamente Cognition Engine.

It represents truth values as intervals, but with different semantics than in Imprecise Probability Theory.

The basic goal of PLN is to provide reasonably accurate probabilistic inference in a way that is compatible with both Term Logic and Predicate Logic, and scales up to operate in real time on large dynamic knowledge bases.

For a discussion of some of the types of reasoning that PLN can carry out see the entry on Inference.

So far, the current version of PLN has been used to do such things as infer biological hypothesis from knowledge extracted from biological texts by language processing, and help an embodied agent figure out how to play "fetch" in a 3D simulation world based on reinforcement learning.

(Note: PLN was previously known as PTL or "Probabilistic Term Logic")

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