[Inquiry] Re: Prospects for Inquiry Driven Systems

[Jon Awbrey]

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PRO.  Note 10

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1.1.2.3.  The Trees and The Forest (cont.)

One of the chief theoretical difficulties that obstructs the unification of
logic and dynamics in the study of intelligent systems can be seen in relation
to this question of how an intelligent agent might generate tentative but plausible
analyses of problems that confront it.  As described here, this requires a capacity
for identifying middle grounds that ameliorate or mollify a problem.  This facile
ability does not render any kind of demonstrative argument to be trusted in the
end and for all time, but is a temporizing measure, a way of locating test media
and of trying cases in the media selected.  It is easy to criticize such practices,
to say that every argument should be finally cast into a deductively canonized form,
harder to figure out how to live in the mean time without using such half-measures
of reasoning.  There is a line of thinking, extending from this reference point
in Plato through a glancing remark by Aristotle to the notice of C.S. Peirce,
which holds that the form of reasoning required to accomplish this feat is
neither inductive nor deductive and reduces to no combination of the two,
but is an independent type.

Aristotle called this form of reasoning "apagogy" ('Prior Analytics', 2.25)
and it was variously translated throughout the Middle Ages as "reduction" or
"abduction".  The sense of "reduction" here is just that by which one question
or problem is said to reduce to another, as in the AI strategy of goal reduction.
Abductive reasoning is also involved in the initial creation or the apt generation
of hypotheses, for instance, in diagnostic reasoning.  Accordingly, it is natural
that abductive reasoning has periodically become a topic of interest in AI and
cognitive modeling, especially in the effort to build expert systems that can
simulate and assist diagnosis, whether in human medicine, in auto mechanics,
or in electronic trouble-shooting.  Recent explorations in this vein are
exemplified by (Peng & Reggia, 1990) and (O'Rorke, 1990).

But there is another reason why the factorization problem presents an especially
acute obstacle to progress in the system-theoretic approach to AI.  When the states
of a system are viewed as a manifold it is usual to imagine that everything factors
nicely into a base manifold and a remainder.  Smooth surfaces come to mind, a single
clear picture of a system that is immanently good for all time.  But this is how an
outside observer might see it, not how it appears to the inquiring system that is
located in a single point and has to discover, starting from there, the most fitting
description of its own space.  The proper division of a state vector into basic and
derivative factors is itself an item of knowledge to be discovered.  It constitutes
a piece of interpretive knowledge that has a large part in determining exactly how
an agent behaves.  The tentative hypotheses that an agent spins out with respect to
this issue will themselves need to be accommodated in a component of free space that
is well under control.  Without a stable theater of action for entertaining hypotheses,
an agent finds it difficult to sustain interest in the kinds of speculative bets that
are required to fund a complex inquiry.

States of information with respect to the placement of this fret or fulcrum
can vary with time.  Indeed, it is a goal of the knowledge directed system
to leverage this chordal node towards optimal possibilities, and this will
normally require a continuing interplay of experimental variations with
attunement to the results.  Therefore it seems necessary to develop a
view of manifolds in which the location or the depth of the primary
division that is discovered to be effective in explaining behavior
can vary from moment to moment.  The total phenomenal state of a
system is its most fundamental reality, but the way that these
states are connected to make a space, with information that
metes out distances, portrays curvatures, and binds fibers
into bundles -- all of this is an illusion projected onto
the mist of individual states from items of code in the
knowledge component of the current state of the system.

Jon Awbrey

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