Thank you Nil,

short remarks: -- what you explain below, is it clearly written up in some
README and/or wiki page? If not, can I get you to do that?

Does the chainer stop, soon as it finds some solution, or does it continue
up until some user-specified depth?

A related but unrelated FYI remark... for the language learning project, we
need to be able to create "random languages", for testing purposes: create
a random syntax, then create random sentences, then see if the learner can
accurately learn the random syntax.

For generating speech, we need to do something similar, but instead use a
fixed (English language) syntax, and include fixed words (e.g. "pizza",
"verb-to-eat", and "Ben") and generate a syntactically-valid sentence that
includes those words.

I'm planning to start a new git-repo "real soon now" that will provide code
for above. The funny thing is -- the algo to accomplish this sounds a lot
like the backward chainer. But with my usual twist -- the rules are not
rules with variables in them, they are the "jigsaw puzzle pieces" with
connectors in them.  Assembly continues until there are no more unconnected
jigsaw-puzzle tabs.  Instead of unification and beta-reduction, one
connects together "connectors".

I think it would be interesting to clarify, via direct examples, if somehow
the jigsaw-puzzle-piece concept can be mapped into URE rules, and if the
backward chainer could be used to perform this generation.  I mean, I know
everyone has lots to do, but ... this should be done...

--linas

On Wed, Mar 4, 2020 at 6:59 AM 'Nil Geisweiller' via opencog <
[email protected]> wrote:

> On 3/2/20 12:57 PM, Alexander Gabriel wrote:
> >
> >     No, it typically doesn't need to execute the link it is doing
> inference
> >     on, rather it runs inference trees constructed from inference rules
> >     that
> >     produces the target, including updating its TV. Whether this is fast
> or
> >     slow depends on the inference trees, not so much the target.
> >
> >
> > I'm not sure I understand, don't the inference trees result from the
> > interaction between the target and the rulebase?
>
> Yes, but that's a static interaction. Basically, in the case of backward
> chaining, the target is syntactically unified with the conclusion of
> some rule, then some simple inference tree like
>
> premise_1
> ...
> premises_n
>
> |-
>
> target
>
> is created, then one of the premises is syntactically unified with
> another rule to create a bigger inference tree, etc.
>
> Every time an inference tree is created it is executed, and results are
> collected, and returned the user at the end.
>
> Nil
>
> >
> >
> >      >     If you add a type declaration (I believe the python bindings
> >     allow you
> >      >     that), it should speed up the reasoning as well.
> >
> > Most of the atoms I add are ConceptNodes (200-300 at the moment and
> > almost all of them are places), so restricting chaining to those doesn't
> > help me much. In any case I'm already doing that.
> >
> >
> >     for instance the frog example show an example of variable
> >     declaration in
> >     a backward chainer query
> >
> >
> https://github.com/opencog/ure/tree/master/examples/ure/frog#backward-chainer
> >     <
> https://github.com/opencog/ure/tree/master/examples/ure/frog#backward-chainer
> >
> >
> >
> > Yes, that's the same kind of variable declaration that I use above. The
> > VariableList from the GetLink I also give to the backward chainer.
> > What happens for different variations of variable declarations provided
> > for the getlink and the backward chainer, I've detailed in the last post.
> >
> > Best,
> > Alex
> >
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