Response to Mike Tintner’s  Thu 10/4/2007 7:36 AM post:

I skimmed “LGIST: Learning Generalized Image Schemas for Transfer Thrust D
Architecture Report”, by Carole Beal and Paul Cohen at the USC Information
Sciences Institute.  It was one of the PDFs listed on the web link you
sent me (at http://eksl.isi.edu/files/papers/cohen_2006_1160084799.pdf).
It was interesting and valuable.  I found its initial few pages a good
statement of some solid AI ideas.  Its idea of splitting states based on
entropy is a good one, one that I have myself have considered as a guide
for when and where in semantic space to split models and how to segment
temporal representations.

But the system appears to be a pretty small one.  It appears to start out
with a fair number of relatively abstract programmer-defined concepts
(shown in Table One), which is probably necessary for what it is trying to
accomplish on the hardware it is trying to accomplish it on.  But is very
different than a more human brain approach, that would be able to learn
most of those concepts itself, and thus would probably have them be more
grounded.  It starts with a lot of the hard work necessary for the type of
problem it is trying to solve already done (which is very helpful for what
it is trying to do), but which at least suggests a question about how well
it will be able to learn in areas where that type of hard work has not
already been done for it.

Edward W. Porter
Porter & Associates
24 String Bridge S12
Exeter, NH 03833
(617) 494-1722
Fax (617) 494-1822
[EMAIL PROTECTED]



-----Original Message-----
From: Mike Tintner [mailto:[EMAIL PROTECTED]
Sent: Thursday, October 04, 2007 7:36 AM
To: agi@v2.listbox.com
Subject: [agi] Another AGI Project


Another AGI project -some similarities to Ben's. (I was not however able
to
play with my Wubble - perhaps you'll have better luck). Comments?

http://eksl.isi.edu/cgi-bin/page.cgi?page=project-jean.html


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