On 5/12/07, Richard Loosemore <[EMAIL PROTECTED]> wrote:
Pei Wang wrote:
> In a broad sense, almost all inference in NARS is analogy --- in a
> term logic, each statements indicates the possibility of one term
> being used (in certain way) as another, and inference on these
> statements builds new "can be used as" relations (which technically
> are called inheritance, similarity, etc) among terms.
>
> In a narrow sense, NARS has an analogy rule which takes "X and Y are
> similar" and "X has property P" as premises to derive a conclusion "Y
> has property P" (premises and conclusions are all true to various
> degrees). See http://nars.wang.googlepages.com/NARS-Examples-SingleStep.txt
> for concrete examples by searching for "analogy" in the file.
>
> For the analogy with the form "X:Y = Z:?", NARS needs more than one
> step. It first looks for a relation between X and Y, then looks for
> Z's "image" under the relation.
Hmmmm....
But do you think this captures *all* of the idea of what "analogy" is
the human case? Most of it?
Of course it cannot be so simple, but I do think in all types of
analogy there is a step that draw a conclusion based on a perceived
similarity.
In practical situations, what makes things complicated is to filter
out a meaningful analogy from many valid-but-useless ones, which is
not a one-step process, and don't depends on a single rule.
How would you say that this squared with Hofstadter's ideas about what
analogy might be?
Roughly speaking, both of me and Hofstadter think
(1) "analogy" means to use one concept as another,
(2) analogy play a central role in cognition,
(3) analogy must be based on a fluid concept representation, which
allows the meaning of a concept to be context-sensitive, and at the
same time not arbitrary.
The difference between us is that I choose to do it in a logical
framework, and in his own works (like Copycat) he doesn't use logic.
For a more detailed discussion, read the following paper:
A logic of categorization, by Pei Wang and Douglas Hofstadter
Journal of Experimental & Theoretical Artificial Intelligence, Vol.18,
No.2, Pages 193-213, 2006
Looking for a relation between X and Y is all very well, but one of the
things that DRH is fond of telling us is that not just any old relation
will do. And if there are a (quasi-)infinite number of possible
relations between X and Y, doesn't the selection of the appropriate one
become the heart of the analogy process, rather than just a subsidiary
step? (In just the same way that reasoning systems in general need to
have an inference control mechanism that, in practice, determines how
the system actually behaves)?
It depends on what you call "heart".
The analogy rule in NARS doesn't choose premises for itself, so the
rule alone doesn't do much without the other mechanisms in the system.
I'll say both the inference rules and the control mechanism are
crucial, though the control mechanism is not designed to support
analogy only, but to support all kinds of inference. Therefore, when
people ask for analogy in NARS, I'll point to the rule, though
everything in the system is relevant.
What I am getting at here is that I think the concept of an "analogy
mechanism" has not even become clear yet, and so for some people to say
that they believe that their systems already have a kind of analogy
mechanism is to jump the gun a little.
Yes, we use the same term for different things, just like NARS does. ;-)
In my system, all relationships can be "opened up" by being operated on,
but there is no fixed class of operators that does the job: instead
they are built on the fly, in a manner that is sensitive to context.
That is fine, but you still have a fixed procedure to build the
operators, right? Can you give us an example about how analogy is
handled in your system?
So the nature of X, Y and also Z will be able to have a diffuse effect on
the operator construction process that is trying to find a good
relationship between X and Y. The process of "finding" an operator can
have general meta-operators that govern how the process happens (in
other words there can be "general, analogy-finding strategies"). Those
meta-operators could be called the thing that "is" the analogy
mechanism, but that would be an oversimplification.
Can you be more specific here? What is the "nature" of a concept X?
How can it contribute to operator construction? What is a "good
relationship" between concepts?
Pei
-----
This list is sponsored by AGIRI: http://www.agiri.org/email
To unsubscribe or change your options, please go to:
http://v2.listbox.com/member/?member_id=231415&user_secret=fabd7936
