From: cmiller@engin.umich.edu (Craig Miller)
Subject: Re: Questions about SOAR
Date: Tue, 28 Jul 92 12:47:11 EDT
Organization: AI Lab, The University of Michigan
Reply-To: cmiller@caen.engin.umich.edu


I can give my interpretation of Soar in answering the following answers.  I must caution 
there can well be other opinions and I'm not necessarily following the "party line"
with my answers.  Perhaps others in the Soar community will answer differently.

In article <1992Jul27.181203.9316@ils.nwu.edu> pautler@ils.nwu.edu (David Pautler) writes:

   1) Where does the initial rule set come from?  I understand that chunking
   provides a way of forming new rules by concatenating and backtracing through
   older rules, but no account was given of how basic domain knowledge was
   acquired (e.g. basic comprehension operators for English).  Is it all
   considered innate?

Currently, the general practice is to write domain-dependent rules (i.e. productions)
that perform the specified task.  Usually these productions also make use of some domain-
independent productions which are loaded at the initial start-up.

I've heard of three different explanations on the origin of the productions: 1) both
domain dependent and independent productions represent innate knowledge, 2) the
domain-independent productions are innate from which domain-dependent knowledge is
acquired, and 3) the Soar architecture must still evolve so that all necessary innate
knowledge is incorporated within the architecture.

I believe the third answer is the ultimate goal, but it's not clear to me if the entire
Soar community backs it.


   2) When *don't* humans chunk?  Prof. Newell mentions on the tape that SOAR
   chunks in some places where humans don't.  There also seems to be a
   theoretical limit on how much SOAR can declaratively represent its own
   processing, and thus on what it can chunk on.  Is there empirical
   evidence showing a definite boundary between content we do chunk on and
   content we don't?

I don't recall the case where Soar chunks and humans don't.  However, Soar chunks 
over impasses and since the problem solving determines when impasses will occur,
the problem solving plays a heavy hand in determining what Soar chunks.  

I don't understand the theoretical limit part of the question.  Given the impasse 
and its resolution, it is not computationally unreasonable to trace through
the resolution's dependencies.

   3) Why make impasse resolution procedural rather than declarative?  Since
   SOAR runs to quiescence, and impasse resolution knowledge can presumably
   be represented declaratively, making impasse resolution procedural rather
   than just another part of the rule set seems pretty arbitrary to me.

This question doesn't make sense to me.  In resolving the impasse, the rule set
IS applied (i.e. productions fire; operators are applied).  This is considered
to be procedural.

   4) Is all new information ("after" perceptual processing) the result of
   inference?  Similarity judgements (e.g. Dedre Gentner's model) and
   likelihood judgements (e.g. Kahneman & Tversky's model) add information
   but aren't based on inference.  I was surprised that Prof. Newell didn't
   mention this in any way.  Do SOAR researchers plan to incorporate non-
   inferential mechanisms?

Soar suggests that similarity judgments arise from the procedural application of
operators.  Rather than having activation levels, match intensities, etc., Soar's
approach focuses on procedural concepts such as the amount of search required 
for recognition or recall.

My concept learning work has focussed on how typicality effects (as manifested by 
response time) can be modeled with a so-called "inference mechanism."

Craig Miller
U. of Michigan
Newsgroups: sci.cognitive