From: pautler@ils.nwu.edu (David Pautler)
Subject: Re: Questions about SOAR
Organization: The Institute for the Learning Sciences
Date: Wed, 29 Jul 1992 14:57:35 GMT

In article <CMILLER.92Jul28114711@raven.engin.umich.edu>, cmiller@engin.umich.edu (Craig Miller) writes:
> 
> 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.

First of all, thanks for your response.  As a framework for a unified
theory, SOAR is very interesting to me.

> 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?
> 
[summary: initial rule set is provided by the programmer]
> 
> 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.

(Don't you mean that it must evolve so that only innate human knowledge is
innately present in SOAR, and the rest acquired?)

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

That's the answer I expected; it's the best answer I'm aware of.  But I'm
concerned that so much of AI depends upon the fact that some undiscovered
mechanism will be able to extract this basic knowledge, yet no one appears
to be seriously working on the problem.  It's surprising to me that so many
claim that "learning is just compilation".

>    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?
> 
[some deleted]
> 
> 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.

I think the situation is analogous to a manager looking over the shoulder of
a factory worker, recording the worker's procedure and efficiency.  But
something can be learned from observing the manager, too, so a manager is
assigned to watch him, and so on to infinity.  Since factories can't actually
implement this "ideal" situation, we should be careful to describe how factories
actually handle the problem of monitoring themselves.

The mind can't declaratively represent all of its own processing, so chunking
is limited by what it can chunk on.  I'm interested in empirical evidence of
where the boundary lies.

>    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.

(I don't understand the relevance of your last sentence.  Is it a rhetorical
question?)

I was asking why *recognition* of impasses and the search for a new problem
space were procedural.  In the tape, Prof. Newell said impasse resolution
occurred only after quiescence was reached; I don't see why the whole process
of impasse resolution couldn't be made part of the rule set, so quiescence would
be guaranteed to coincide in an actual solution.

>    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.

But how is successful recognition or recall judged?  By counting shared features
in an array?  Structure-mapping?  "Amount of search required" sounds like
Kahneman & Tversky's availability heuristic, in fact.

I'm glad to hear SOAR uses this kind of heuristic, but the impression I got from
the tape was that all processing was done by inference or impasse resolution.
Do similarity or likelihood judgements play any role in choosing one plan over
another?  Are these judgements recorded declaratively so they can be used by
the chunking mechanism?

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

Would you mind explaining how this works?  The model of typicality judgement
I've heard most frequently is that given a category, we survey all the instances
to find the most common shared features.  We then look for an instance that has
a high number of these shared features and a low number of idiosyncratic
features.  Those instances are prototypes, and their role as prototypes
"calcifies" over time so that they begin to define the category itself.

> Craig Miller
> U. of Michigan

If you're going to the CogSci conference, perhaps we can meet to talk there.

	-dp-
Newsgroups: sci.cognitive