Hello,
On 11/7/18 4:44 PM, Ivan Vodišek wrote:
My approach is to manually resolve some NP complete
<https://en.wikipedia.org/wiki/NP-completeness> problems such are
Boolean SAT or Traveling salesman in polynomial time. I have some
indices that it is a possible task.
I suspect you'd only discover subclasses of inputs for which P = NP.
But that doesn't mean it wouldn't be useful or insightful.
(My apologies to Singularity.net
crew due to mess that P = NP would do to AGI Coin. But there are so much
Quite the contrary, any knowledge that can be used to decrease
algorithmic complexity is extremely welcome. Consider that you're
talking to people who are banging their heads against the wall of
complexity on a daily basis. ;-)
Nil
Obviously for a finite set of inputs, one can turn any complex
algorithm into a logarithmic one (think of a pre-calculated binary
decision tree, where each branch is a bit describing the input and
each leaf is the solution). But it should still be possible to learn
an actual algorithm rather than a finite giant decision tree, that
performs worse that log, is more compact, but performs better than NP
for a bunch of real-world problems.
I think of algorithms as a possible compressed forms of their output.
sri, 7. stu 2018. u 14:19 'Nil Geisweiller' via opencog
<[email protected] <mailto:[email protected]>> napisao je:
Hello Ivan,
If P = NP it would mean that, given a problem reframed as reasoning
such that its solution is a proof p of size s, one could construct p
in a polynomial time of s, which sounds very doubtful.
That's in theory, in practice however I think we could make P = NP for
some class of inputs via clever use of meta-learning (such as
inference control meta-learning that we're experimenting within
opencog, see
https://blog.singularitynet.io/introspective-reasoning-within-the-opencog-framework-1bc7e182827).
In fact I had this dream where we could have a sequence of NP problems
and progressively learn how to solve them in P.
Obviously for a finite set of inputs, one can turn any complex
algorithm into a logarithmic one (think of a pre-calculated binary
decision tree, where each branch is a bit describing the input and
each leaf is the solution). But it should still be possible to learn
an actual algorithm rather than a finite giant decision tree, that
performs worse that log, is more compact, but performs better than NP
for a bunch of real-world problems.
Nil
On 11/6/18 7:00 PM, Ivan Vodišek wrote:
> Hello everyone :)
>
> I have a question regarding to my independent research relating to
> OpenCog. I read somewhere (I really don't remember where) that if
P = NP
> <https://en.wikipedia.org/wiki/P_versus_NP_problem> then it would be
> beneficial to AI in general.
>
> There are science fields which would obviously benefit if P = NP.
But my
> question is: how would specifically OpenCog benefit from that
solution?
> Somehow, it should be a matter of reducing a large number of
possible
> combinations, but I don't really see were would AI fit into this
> equation. Googling around didn't produce anything interesting, so
I'm
> making a post to this OpenCog community in a hope for an answer.
>
> Thank you all for your time,
> Ivan V.
>
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