Yeah but if P & P are negative ?

Le mer. 7 nov. 2018 à 14:19, 'Nil Geisweiller' via opencog <
[email protected]> a écrit :

> 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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