[MUD-Dev] Personality modelling
J C Lawrence
claw at under.engr.sgi.com
Wed Apr 15 12:39:05 New Zealand Standard Time 1998
Forwarded for Cynbe who is currently unable to dpost for technical
Date: Tue, 14 Apr 1998 21:42:03 -0500
Message-Id: <199804150242.VAA04958 at laurel.actlab.utexas.edu>
From: Cynbe ru Taren <cynbe at muq.org>
To: claw at under.engr.sgi.com
Subject: [MUD-Dev] Personality modelling
| > http://www.erasmatazz.com/Library/JCGD_Volume_7/Personality_Modelling.html
| <nod> I'm working my way thru the JCGD pages. Lotta good stuff.
| I'll excerpt here as I get the chance.
A nice collection I wasn't aware of -- thanks!
Two quick notes:
1) Above link doesn't work for me with Netscape, at least -- needs
to be lower-case "library".
2) The personality design vector notion touches on some pondering I've
been doing re AI over the last few decades. I'll predict that
(A) You'll wind wanting your values to be two-valued real vectors (or
else complex numbers), so that you can distinguish value from
intensity. E.g., weakly suspecting that someone is very evil is
not the same thing as being very confident that they are very
mildly evil. Having separate direction and magnitude available
for a value makes this distinction easy. (If you go with the
two-valued real vector approach, the generalization from one-D
to 2-D or 3-D directions for values is then straightforward.
If you go with complex numbers, you're at a dead end once you
reach quaternions. Then again, I grok neither of them...)
(B) You'll wind up wanting to compare these vectors of values using
an inner-product distance metric rather than a Euclidean distance
metric, because the Euclidean distance metric is dominated by
differences, while the inner-product distance metric is dominated
by similarities -- typically more to the point in these sorts
of situations, in my ignorant impression.
(If anyone knows of a promising, realistic alternative distance
metric to those two, I'd be most interested in hearing about it.
But I have a tantalizing impression one can come close to proving
that there are no more.)
In my impression, projection of long sparse vectors (i.e., inner-
product metric) is a very promising way to do soft, human-seeming
associations/comparisons that feel much more natural and robust
than the classical AI boolean expression stuff. Toss in a little
information theory, and I expect you're really getting somewhere.
But that's straying offtopic. :)
(C) You'll ignore the above two points initially and rediscover
them the hard way. :)
J C Lawrence Internet: claw at null.net
(Contractor) Internet: coder at ibm.net
---------(*) Internet: claw at under.engr.sgi.com
...Honourary Member of Clan McFud -- Teamer's Avenging Monolith...
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