  Barwise and Seligman stated the first principle of information flow:
"Information flow results from regularities in the distributed system." They
represent a distributed system in terms of a classification consisting of a set
of objects or tokens to be classified, a set of types used to classify tokens,
and a binary relation between tokens and types that tells one which tokens are
classified as being of which types. We aim to further this investigation and
proceed with a dynamic or evolving system instead of a static system. We claim
that a classification is a snapshot of a distributed system at a given moment
or context. We then aim to answer the question posed by an evolving context. As
the context or configuration changes, how to regularities evolve. This paper is
a continuation of an investigation we started in \cite{esterlin}, where we
initiated how to capture a dynamism of information flow with a Kripke
structure. Here we develop the same procedure with colored Petri net(CPN). We
first extend the classification concept to multiclassification by replacing its
binary relation between tokens and types with a multi relation: a function from
tok(A) x typ(A) to N, the set of natural numbers. The multiclassification will
unfold into binary classification in order to compute its theory. It turns out
that markings of a CPN are multiclassification; Amalgamating the theories of
those classifications obtained as markings of CPN results in a CPN's knowledge
base.
