Soar, like other "cognitive architectures" (such as ACT-R), is not
designed to directly deal with domain problems. Instead, it is a
high-level platform on which a program can be built for a specific
problem.
On the contrary, Novamente, like other "AGI systems" (such as NARS),
is designed to directly deal with domain problems. To work well,
usually it needs to be trained with domain-specific knowledge, but
such a "training" process is fundamentally different from a
"programming" process.
To me, many other differences, such as the role of learning, follow
from the above difference between "program to work" and "learn to
work".
The current issue of AI Magazine
(http://www.aaai.org/Library/Magazine/vol27.php#Summer) is highly
relevant to this discussion. Especially, the articiles by Langley,
Cassimatis, and Jones&Wray provide good introductions and discussions
about cognitive architectures.
Pei
On 7/13/06, Ben Goertzel <[EMAIL PROTECTED]> wrote:
Thanks, Randy. This is very well put.
Yes, one of the key things missing in rule and logic based AI systems
like SOAR is the learning of new representations to match new
situations and problems.
Interestingly, this is also one of the key things missing in
evolutionary learning as conventionally implemented. My colleague
Moshe Looks has been working on a modified approach to evolutionary
learning that involves automatically learning new representations for
new problems; it is called MOSES and is being written for integration
into Novamente as well as for standalone use. Some information on
MOSES is here if you're curious:
http://metacog.org/doc.html
-- Ben
On 7/13/06, James Ratcliff <[EMAIL PROTECTED]> wrote:
> 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 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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