In response to a suggestion that PM made, I originally replied that
differentiation would be easy.  I guess that is not right.  The reason is
because the method of scalability that I will rely on will be correlation.
I would be using the combinations of different types of *insights* or
*ideas* anyway so these would act as correlations between different types
of data objects that were somehow related.  Correlation is a normal method
of scalability in AI but I will not be limited to using statistical
correlation in my program (although I will use it if I see a good reason to
use it.)  The problem with integration is the problem of integrating
different kinds of relevant insight or information so the integration
problem is also a problem that I expect to find with correlation.  The
solution to integration and to differentiation is to find out more about
the objects being integrated or differentiated. I feel that this method is
also necessary for judgement and awareness (in the basic sense).  So all
these ideas are closely related in my plan. However, what I wanted to say
is that both differentiation and integration are going to be difficult.

Jim Bromer

On Fri, Sep 14, 2012 at 9:42 PM, Piaget Modeler
<[email protected]>wrote:

>  Building on what Stan said, you can also substitute "case" for reason as
> well.  Rules or cases, either will do.
>
> Jean Piaget talks about Differentiation and Integration as the hallmarks
> of intelligence.
>
> Douglas Hofstadter has done a lot of work with analogy (which is one form
> of integration).
> Simple Crossover (from classifier systems) is another form of integration.
>
> PAM-P2 takes both approaches, having an analogizer component to perform
> concept substitution according to specified "rules",
> as well as concept invention by means of crossover during regulation (the
> regulator component governs this).
>
> There are many ways to achieve integration though.  The real trick is
> differentiation.
>
> Cheers!
>
> ~PM.
>
>
> ------------------------------------------------------------------------------------------------------------------------------------------------
>
>
> > Date: Fri, 14 Sep 2012 18:42:42 -0600
> > From: [email protected]
> > To: [email protected]
> > Subject: Re: [agi] Simplistic Test of Reason-Based Reasoning
>
> >
> > Greetings Jim.
> > my reaction to a couple statements contained below -
> >
> >
> > On 09/14/2012 08:06 AM, Jim Bromer wrote:
> > >
> > >
> > > I was wondering if a simple system of reason based reasoning could be
> > > used to start an expanding system of knowledge acquisition. I am not
> > > talking about a human-level AGI program. I am talking about a very
> > > simple, very artificial system to test the viability and the
> flexibility
> > > of the reason-based reasoning strategy for general learning.
> >
> > stan: I like the beginning line, especially the word "simple." A
> > bootstrap kind of program is exactly what is needed.
> >
> > To stick with the simple description, I would avoid the word "reason"
> > and put in "rule." e.g. can a simple rule based system start an
> > expanding system - expanding by virtue of it's acquiring facts and
> > rules. More about this below.
> > >
> > > Reason-based reasoning is just a strategy in which analysis and
> response
> > > to a situation is based on reasons which the AGI program can access.
> >
> > rules no?
> >
> > > .... <section removed >
> > >
> > > So my question is whether or not reason-based reasoning can be used
> > > effectively in a simplistic system to enable the program to make good
> > > reactions based on what it had learned. But I do not fully understand
> > > how human beings are able to adeptly recognize and react to complicated
> > > situations.
> >
> > stan: Would it be fair to rephrase this question? Something like " can
> > one build a simple rule based system that will make good choices based
> > on rules and facts it has acquired?"
> >
> > If so, it seems obvious that the system can only operate on rules and
> > facts acquired.
> >
> > The bigger question is can the system take a fact or rule gathered from
> > it's experience in the environment and place it where it's rule
> > processor finds it at the right time? What are the rules that tell the
> > system how to utilize this 'mined' knowledge?
> >
> > simple huh?
> >
> > >
> > > Analysis and reactions do not only act on some form of output. They can
> > > govern the analysis and reaction modes as well.
> >
> > stan: True, the "output" could simply be a state change in the
> > analyzer. This often is the case when writing a program - branch on a
> > condition...
> >
> > ... portions removed
> > >
> > > One problem that I do not completely understand is how concepts are
> > > integrated. Reason-based reasoning will help but it does not explain
> > > everything. I am thinking about starting with a primitive artificial
> > > language to make the program work a little like a programming method.
> > > However, with reason-based reasoning that is able to act on recognition
> > > and reaction methods there is no reason why I could not experiment with
> > > language acquisition.
> > >
> > stan: If the goal is to understand how concepts are integrated, put on
> > the designer hat and say "how would I design a system that can integrate
> > concepts?" What do the players look like?
> > "Concepts" is a bit nebulous to me. More concrete might be something
> > like " how are rules and facts integrated in the system I am designing?"
> >
> > take care...
> > Stan
> >
> >
> >
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