I can't think of a simple answer for how to determine c

Suppose E1 and E2 are two bodies of evidence...

let

c1 =P(E1 &E2) / P(E1)

c2 =P(E1 & E2 ) / P(E2)

Then the revision formula could be estimated as

n = n1 + n2 - c1*n1

or

n = n1 + n2 - c2*n2

Averaging those two values together would make sense...  The current
formula uses

c = P(E1 & E2) / P(E1 || E2)

n = n1 + n2 - c*min(n1,n2)

which amounts to about the same thing in most cases I guess...

But without some estimates of c1, c2 or c, this is all pretty fudgy...

one could concoct various heuristics for estimating the c values in various
situations.   But it seems there are many more basic issues to solve with
PLN first...



On Wed, Mar 19, 2014 at 7:39 AM, Cosmo Harrigan <notifications@github.com>wrote:

> In opencog/python/pln/rules/formulas.py:
>
> > @@ -473,10 +479,19 @@ def ontoInhFormula(tvs):
> >
> >
> >  def revisionFormula(tvs):
> > +    '''From the PLN Book: We may also heuristically form a count rule
> > +    such as n = n1 + n2 - c min(n1, n2), where the parameter c indicates an
> > +    assumption about the level of interdependency between the bases of
> > +    information corresponding to the two premises. The value c=1 denotes
>
> I think we will need to clarify further how the value 'c' is determined
>
> --
> Reply to this email directly or view it on GitHub<https://github.com/opencog/opencog/pull/610/files#r10731465>
> .
>



-- 
Ben Goertzel, PhD
http://goertzel.org

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