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 direc
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
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 r
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...
> (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 li
(From a former Soar researcher) I don't have the time to get involved in a big discussion board, but just in case nobody else replies I thought I'd send you a couple of sentences. Soar at it's core is a pretty simple beast. It's a very high performance production rule system with built in s
