Just some quick comments. It appears to me that perhaps the primary
topic in question is an ability to generalize or abstract knowledge to
varieties of situations. I would say that for the most part Soar is
very good at *representing* and *using* composable (and therefore
generalized) knowledge representations, but it is not so far Soar's
strong suit to *create* such knowledge representations. There has been
a bit of research in the past to get Soar to do inductive learning, and
those efforts have currently shifted a bit to "stepping outside" the
standard Soar model and integrating in capabilities for reinforcement
learning and episodic learning. However, these efforts are in early
stages. For the most part when we want nice generalized knowledge in
Soar (which is often, when we are trying to build robust cognitive
models or intelligent agents), we engineer the abstractions and
knowledge representations directly into the system.
One strength of Soar (in my opinion) is that it encourages "composable"
knowledge representations that can rapidly "assemble themselves" (again
with the proper hard-coded engineering) into wide varieties of actions
or solutions to problems. So for example, rather than having 1000
different schemas for opening different kinds of doors, or one
monolithic high-level schema, the typical approach in Soar would be to
engineer independently the various small steps that can compose into a
variety of door-opening schemas, and then layer on top of those
low-level actions a hierarchy of potential situations (or partial
situations) in which the various steps would be appropriate to execute.
Done "correctly", this can lead to a robust reasoning system that can
easily switch its behavior as the environment changes.
However, there is a big caveat here. Although I claim (and believe)
that Soar encourages the development of such robust models, it does not
*require* you to represent your knowledge that way. It is certainly
easy to build brittle systems in Soar, containing knowledge that is not
abstracted well. An engineer has to do the work of finding the right
abstractions, which it sounds to me like where some of the focus is in
Novamente. Once you have some reasonable abstractions, though, Soar
provides a good engine for representing the knowledge in modular and
efficient ways.
Randy Jones
Ben Goertzel <[EMAIL PROTECTED]> wrote:
One of the key ideas underlying the NM design is to fully integrate
the top-down (logical problem solving and reasoning) based approach
with the bottom-up (unsupervised, reinforcement-learning-based
statistical pattern recognition) based approach.
SOAR basically lies firmly in the former camp...
-- Ben
On 7/12/06, Yan King Yin <[EMAIL PROTECTED]> wrote:
>
> > (From a former Soar researcher)
> > [...]
> > Generally, the bottom-up pattern based systems do better at noisy pattern
> recognition problems (perception problems like recognizing letters in
> scanned OCR text or building complex perception-action graphs where the
> decisions are largely probabilistic like playing backgammon or assigning
> labels to chemical molecules). Top-down reasoning systems like Soar
> generally do better at higher level reasoning problems. Selecting the
> correct formation and movements for a squad of troops when clearing a
> building, or receiving English instructions from a human operator to guide a
> robot through a burning building.
> > [...]
> > Doug
>
>
> From what I read, Soar also deals with (or has provisos to deal with)
> sensory processing, otherwise it wouldn't be the "unified cognitive
> architecture" as Allen Newell has intended it to be.
>
> The difference in emphasis between Novamente on perceptual learning and Soar
> on top-down reasoning, may be real but ideally it should not be accepted
> prima facie . IMO these 2 emphases should be integrated seamlessly.
>
>
> YKY ________________________________
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Thank You
James Ratcliff
http://falazar.com
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