  Today, a wide variety of probabilistic and expert AI systems used to analyze
real world inputs such as unstructured text, sounds, images, and statistical
data. However, all these systems exist on different platforms, with different
implementations, and with very different, often very specific goals in mind.
This paper introduces a concept for a mediator framework for such systems and
seeks to show several architectures which would support it, potential benefits
in combining the signals of disparate networks for formalized, high level logic
and signal processing, and its possible academic and industrial uses.
