PLN itself does not invent predicates; as you note, in the OpenCogPrime
architecture that is the job of other MindAgents in the OCP design ...

Speculative concept formation

http://opencog.org/wiki/OpenCogPrime:SpeculativeConceptFormation

plus the predicatization of schema learned via MOSES and other methods ...
See also

http://opencog.org/wiki/OpenCogPrime:IncorporatingOtherCognitiveProcessesIntoInference

As for how PLN would extrapolate a series -- PLN is basically a toolkit not
an AI system, so it could be used to approach this in a load of different
ways

A more natural way to approach that sort of problem in OpenCog might be
using MOSES with PLN in a subordinate role, helping MOSES.  But in some
cases direct deployment of inference might be the best approach...

ben



On Mon, Aug 4, 2008 at 10:53 PM, Abram Demski <[EMAIL PROTECTED]> wrote:

> As I understand it, FOL is only Turing complete when
> predicates/relations/functions beyond the ones in the data are
> allowed. Would PLN naturally invent predicates, or would it need to be
> told to specifically? Is this what "concept creation" does? More
> concretely: if I gave PLN a series of data, and asked it to guess what
> the next item in the series would be, what sort of process would it
> employ?
>
> Thanks,
> --Abram Demski
>
> On 8/4/08, Ben Goertzel <[EMAIL PROTECTED]> wrote:
> > On Mon, Aug 4, 2008 at 6:10 PM, YKY (Yan King Yin) <
> > [EMAIL PROTECTED]> wrote:
> >
> >> On 8/5/08, Ben Goertzel <[EMAIL PROTECTED]> wrote:
> >> >
> >> > As noted there, my impression is that PILP could be implemented within
> >> OpenCog's PLN backward chainer (currently being ported to OpenCog by
> Joel
> >> Pitt, from the Novamente internal codebase) via writing a special
> scoring
> >> function ...
> >>
> >>
> >> Yes, I think the inductive search is somewhat similar to backward
> >> chaining,
> >> except that the steps in the inductive search can *create* rules,
> whereas
> >> in
> >> backward chaining you're applying *existing* rules.
> >>
> >
> >
> > Yes, but in PLN/ OpenCogPrime backward chaining *can* create hypothetical
> > logical relationships and then seek to estimate their truth values
> >
> > See this page
> >
> > http://opencog.org/wiki/OpenCogPrime:IntegrativeInference
> >
> > and the five pages linked to from it (at the top)
> >
> >
> >>
> >> We need a scoring function, but I have not thought about this yet.
> >>
> >> I think the hardest part is actually in generating the search tree.  You
> >> see, in first-order logic, rules can involve many predicates, predicates
> >> may
> >> have variables as arguments, and the arguments may even have complex
> terms
> >> involving functions.  So the combinatorial explosion is severe.
> >>
> >
> > The purpose of the scoring function is precisely to attempt to manage
> this
> > combinatorial explosion.
> >
> >
> >>
> >> The scoring function may provide a "gradient" over the search space, so
> >> you
> >> suggested to use hill-climbing.  But I suspect that such a gradient is
> not
> >> useful during the search, because the search space is discrete and
> >> irregular, and the scores probably jump irregularly from node to node.
> >> That's why I suspect that hill-climbing is not useful here.
> >>
> >
> > As noted in one of the pages mentioned above,
> >
> > http://opencog.org/wiki/OpenCogPrime:HebbianInferenceControl
> >
> > I believe that the only solution to this problem is not algorithmic, but
> > architectural: we need to mine the data-store of historical inferences
> >
> > http://opencog.org/wiki/OpenCogPrime:InferencePatternMining
> >
> > and  use this information to provide inductive bias to be used within the
> > scoring function itself.
> >
> > -- Ben G
> >
> >
> >
> > -------------------------------------------
> > agi
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-- 
Ben Goertzel, PhD
CEO, Novamente LLC and Biomind LLC
Director of Research, SIAI
[EMAIL PROTECTED]

"Nothing will ever be attempted if all possible objections must be first
overcome " - Dr Samuel Johnson



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