Now that I Have a Good Theory of Uncertainty, What Else Do I Need?
  Rather than discussing the isolated merits of a nominative theory of
uncertainty, this paper focuses on a class of problems, referred to as Dynamic
Classification Problem (DCP), which requires the integration of many theories,
including a prescriptive theory of uncertainty. We start by analyzing the
Dynamic Classification Problem and by defining its induced requirements on a
supporting (plausible) reasoning system. We provide a summary of the underlying
theory (based on the semantics of many-valed logics) and illustrate the
constraints imposed upon it to ensure the modularity and computational
performance required by the applications. We describe the technologies used for
knowledge engineering (such as object-based simulator to exercise requirements,
and development tools to build the Knowledge Base and functionally validate
it). We emphasize the difference between development environment and run-time
system, describe the rule cross-compiler, and the real-time inference engine
with meta-reasoning capabilities. Finally, we illustrate how our proposed
technology satisfies the pop's requirements and analyze some of the lessons
reamed from its applications to situation assessment problems for Pilot's
Associate and Submarine Commander Associate.
