On 10/07/2017 04:39 PM, Linas Vepstas wrote> So here's a completely
different but related idea: First, use a crisp
reasoner to deduce what happens whenever strength>0.9999. Next, do it
again, but now for strength>0.8. (but still using the crisp reasoner:
just take strength>0.8 to mean "true"). This should have a "broader" set
of consequences. Do it again for strength>0.6 - this causes even more
possibilities to be explored.
It seems like these three cases can be treated as "lower bounds" of what
we might expect PLN to find. That these could be used to guide/limit
what PLN explores.
Alternately, if this was fast enough, you could do this 100 times for
100 different truth cutoffs, and build up a distributional TV...
That's an interesting idea. You could
1. Sample the probability of each atom in the KB (axioms) according to
their TV
2. Sample, according to this probabilities, whether the axiom is true or
false
3. Run crisp-PLN over the dicretized theory, save the output
4. repeat 2. N times to obtain a probability of the output
5. repeat 1. M times to obtain a second order probability to regenerate
the output TV
I suppose this type of crisp-PLN monte-carlos simulation should converge
to PLN. The advantage could be real though, assuming PLN complexity
grows with exp(alpha*L), and the complexity of crisp-PLN grows with
exp(beta*L), with beta < alpha, L being the length of the proof, we'd
reach a point where where M*N*exp(beta*L) < exp(alpha*L).
Certainly an idea to keep in mind.
Nil
I find this idea exciting! It seems plausible, doable ...
--linas
Nil
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