"YKY (Yan King Yin)" <[EMAIL PROTECTED]> wrote:
Comparing the problem at hand with SAT may not be very accurate. First, we
need to formulate the problem more clearly -- what exactly are we trying to do.
Then we can estimate whether it's feasible with available computing power.
Also, modern complexity theory has moved beyond P and NP -- to things that I'm
still struggling to learn. For example, there are complexity classes beyond
NP, in the polynomial hierarchy. Also there is the analytical hierarchy which
is different from the polynomial one. Very often I see logical problems being
described in the analytical hierarchy. For example, abduction and induction
are both higher in the analytical hierarchy than SAT.
I guess our problem would involve abductive and inductive learning, so it
would be strictly harder than SAT. No doubt that we'd employ heuristics, so
the worst-case complexity is not a show-stopper. But still, there is the
possibility that the problem would be too hard using realistic computing power.
YKY
AGI might even turn out to be impossible, but I feel that there is a greater
chance that eventually a program that is built to handle more complexity, in
just the right way, will succeed.
I had pretty much rejected the possibility that more logical or rational
systems would result in a significant leap in the field until I became
interested in the feasibility of a general polytime SAT solver. Now I see it
as a logical next step in the trial and error method of research. In other
words, something has been missing in the field logical-rational methods. The
lack of a more efficient general solver actually represents an unbalance in the
use of logical methods in computing. Most of us did not think of in just this
way, but if a reasonable polytime general solver is feasible then it means that
that we can significantly boost computing power through software. Even if this
doesn't produce a significant leap in AI it might produce the overdue next step.
Jim Bromer
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