It's also been pointed out by numerous people that much of the misery in
this world is due to people searching forever for the "perfect" solution
when an immediately available "good enough" would have given them far more
happiness.
----- Original Message -----
From: "Charles D Hixson" <[EMAIL PROTECTED]>
To: <[email protected]>
Sent: Monday, April 30, 2007 3:56 PM
Subject: **SPAM** Re: [agi] MONISTIC, CLOSED-ENDED AI VS PLURALISTIC,
OPEN-ENDED AGI
Bob Mottram wrote:
On 30/04/07, *Mike Tintner* <[EMAIL PROTECTED]
<mailto:[EMAIL PROTECTED]>> wrote:
Best example I can think of is William Calvin saying something
like: "the conscious mind is clearly designed to deal with
problematic decisions, where existing solutions won't work. The
smartest mind is the one that can find the correct answer to those
problems." Well, that's a definite self-contradiction. There is
no correct answer to problematic decisions, only a calculated gamble.
When dealing with probabilities there may be no single correct answer,
but a variety of possible answers with probabilistic weightings assigned
to them (the calculated gamble). For example, when you have a robot
navigating around using its senses the raw sense data is always subject
to some degree of noise or quantisation. Over time the exact same
sensory input could correspond to multiple possible positions of the
robot, but some will be more probable than others. The uncertainty in
sensing and the movement of the robot can be modelled using mathematical
curves called probability density functions. Much of the time the
functions used to represent the uncertainty are gaussian ("normal")
distributions, although this isn't always the case.
More generally, in problems of a very common type (possibly several
different types) the optimal solution is computationally intractable, even
when it is precisely definable. In such cases the practical choices are
between "good enough", "almost optimal", and "not getting the answer in
time to use it". "Good enough" is frequently so much quicker to calculate
that it's the best choice for a quick reaction. "Almost optimal"
generally requires careful analysis of the problem, which means that you
had better have predicted that the problem was going to show up ahead of
time. "Optimal" is generally a very poor choice, even for library
code...though occasionally it is the best choice.
Well, reading this over it seems that "optimal" has been given a rather
poor definition, when viewed in the context of these classes of problems,
but that's the term used by my Linear Programming professor a few decades
ago. Also note that "good enough" isn't defined, but has to be a judgment
call in every particular case. A *quick* judgment call.
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