In modeling intelligent systems, whether we are trying to understand
a natural system or engineer an artificial system, there has long been
a tension or trade-off between dynamic paradigms and symbolic paradigms.
Dynamic models take their cue from physics, using quantitative measures
and differential equations to model the evolution of a system’s state
Symbolic models use logical methods to describe systems and their agents in
qualitative terms, deriving logical consequences of a system’s description or
an agent’s state of information. Logic-based systems have tended to be static in
character, largely because we have lacked a proper logical analogue of differential
calculus. The work laid out in this series of posts is intended to address that lack.
Note: The links below will take you to the current OEIS Wiki version of a project
I began under the auspices of a Systems Engineering program at Oakland University.
I'm about to be shanghaied into a mess of long put off chores but some time this
summer I hope to get back to revising the text, redoing the graphics messed over
by long ago Mac to PC platform changes, and serializing the content to my blog.