Thanks Jim

Good thoughts you shared. Stuff to definitely think about. 

I think, at a certain quantum level, undecidability may even be a constant. The 
formula for Satisfiability would perhaps then have to adapt, and failing which, 
would be deemed unsatisfiable. It might be more a problem of standard logic 
then, than satisfiability per se, or degrees of both. I think it could be a 
useful test either way, depending on how it was deployed. 

In my view, the optimal test of any pattern can be discovered in the properties 
of such a pattern's pattern. To my mind, that should then be the first, logical 
test. 

Affecting established complexity may hold N potential for tipping the scales to 
a new dimension of exponential complexity, meaning it could induce the net 
value of a system from a measured state of "satisfiability" to a measured state 
to "unsatisfiability", as an event of discontinuous change (e.g., a person 
going insane from distress), and if the force was large enough, as a form 
changer (committing suicide) - to use a negative example. Thus, the "affecting" 
should be able to manage such a state change within a system as a mechanism of 
survival, which could become satisfiable in its operational proof. 

In other words, within AGI, a terrestrial machine state of non satisfiability 
may even be a preferred state, in order to "embrace" truly random, 
unpredictable, environmental dynamicism, but its function could still be 
expressed in relatively, satisfiable terms.   

Rob    

> Date: Tue, 17 Feb 2015 23:52:03 -0500
> Subject: Re: [agi] SAT and Dynamic Programs of Models
> From: [email protected]
> To: [email protected]
> 
> On Tue, Feb 17, 2015 at 10:26 PM, Jim Bromer via AGI <[email protected]> wrote:
> > I started wondering about how a good Satisfiability model might be
> > used with AGI.
> 
> It wouldn't because the hard problems in AI like vision and language
> are not NP-hard. The more useful application would be breaking nearly
> all forms of cryptography. (One time pad would still be secure).
> 
> 
> -- 
> -- Matt Mahoney, [email protected]
> 
> 
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