I constructed a while ago (mathematically) a detailed mapping from
Novamente Atoms (nodes/links) into n-dimensional vectors.  You can
certainly view the state of a Novamente system at a given point in
time as a collection of n-vectors, and the various cognition methods
in Novamente as mappings from R^k -> R^m ...

So, based on this, I can see that an n-dimensional representation of
knowledge and mind-states is definitely feasible...

However, I don't see that this, in itself, really gets you much of anything...

I'm afraid the analogies between vector space operations and cognitive
operations don't really take you very far.

For instance, you map conceptual blending into quantitative
interpolation -- but as you surely know, it's not just **any**
interpolation, it's a highly special kind of interpolation, and to
formalize or teach an AI system this "specialness" is nontrivial
whether your underlying k-rep is n-vectors or probabilistic logic
formulas or whatever...

-- Ben


On 11/25/06, J. Storrs Hall, PhD. <[EMAIL PROTECTED]> wrote:
On Friday 24 November 2006 10:26, William Pearson wrote:
> On 24/11/06, J. Storrs Hall, PhD. <[EMAIL PROTECTED]> wrote:
> > The open questions are representation -- I'm leaning towards >CSG
>
> Constructive solid geometry? You could probably go quite far towards a
> real world navigator with this, but I'm not usre how you plan to get
> it to represent the internal state of other systems, so it can try to
> predict what actions people may take due to their emotions etc. I'm in
> favour of multiple and changeable representations myself. Quite how to
> integrate them all into a somewhat coherent world view is an
> interesting problem though.

Imagine a space in which a frame is a single point. This is standard practice
in physical science, where the space is sometimes referred to as a "phase
space." As the frame evolves in time, it will describe a trajectory in the
space. Similar, slightly variant frames will occur near the original one in
the space.

Combining all possible trajectories under a given constraint produces a
subspace that is a hypersurface in the original one, like the "potential
energy surfaces" seen in physics and chemistry. (The spaces typically have
numbers of dimensions ranging from thousands to Avagadro's number...)

A frame can be thought of a sentence, a constraint on the set of possible
worlds. The surface can be thought of as a space of possible worlds, and
regions in the space represent sentences. Doing CSG on regions in the space
is semantically equivalent to using propositional connectives on sentences --
AND = intersection, OR = union, etc.

The reason for going to all this trouble is that not only propositional logic,
but many other useful conceptual operations have fairly simple geometric
cognates in this representation. Conceptual blending is just interpolation.
Various kinds of prediction are extrapolation. Some fairly standard forms of
metaphor reduce to geometric quadrature:
if A is to B as C is to D, A = B + C - D.

Finding abstractions reduces to projective geometry, or rather doing
abstraction does -- finding useful ones is really the holy grail! The really
cool thing about n-spaces as a representation scheme is that they do ground
out in simple geometric representations of the physical world at the concrete
end, but extend more or less seamlessly into the abstract. Some
intermediates: from the 3-d representation of a robot's surroundings, to the
n-DOF configuration space of its possible positions, to one with it and all
the other objects around, to a Lagrangian space for dynamics where
least-energy action planning turns into path-finding.

Now take 10 million or so associative memories (think cortical columns), each
of which records trajectories of a different frame-type. Most of these are
abstractions that are defined in terms of transformations and combinations of
other frames, of course. Each one can be used as a CBR-style predictor,
planner, and modeller. Hook them all together and you have a fair dinkum
engine of reason and seat of the soul.

Josh

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