Ben,

You assert that Pei is forced to make an assumption about the
regulatiry of the world to justify adaptation. Pei could also take a
different argument. He could try to show that *if* a strategy exists
that can be implemented given the finite resources, NARS will
eventually find it. Thus, adaptation is justified on a sort of "we
might as well try" basis. (The proof would involve showing that NARS
searches the state of finite-state-machines that can be implemented
with the resources at hand, and is more probable to stay for longer
periods of time in configurations that give more reward, such that
NARS would eventually settle on a configuration if that configuration
consistently gave the highest reward.)

So, some form of learning can take place with no assumptions. The
problem is that the search space is exponential in the resources
available, so there is some maximum point where the system would
perform best (because the amount of resources match the problem), but
giving the system more resources would hurt performance (because the
system searches the unnecessarily large search space). So, in this
sense, the system's behavior seems counterintuitive-- it does not seem
to be taking advantage of the increased resources.

I'm not claiming NARS would have that problem, of course.... just that
a theoretical no-assumption learner would.

--Abram

On Tue, Oct 28, 2008 at 2:12 PM, Ben Goertzel <[EMAIL PROTECTED]> wrote:
>
>
> On Tue, Oct 28, 2008 at 10:00 AM, Pei Wang <[EMAIL PROTECTED]> wrote:
>>
>> Ben,
>>
>> Thanks. So the other people now see that I'm not attacking a straw man.
>>
>> My solution to Hume's problem, as embedded in the experience-grounded
>> semantics, is to assume no predictability, but to justify induction as
>> adaptation. However, it is a separate topic which I've explained in my
>> other publications.
>
> Right, but justifying induction as adaptation only works if the environment
> is assumed to have certain regularities which can be adapted to.  In a
> random environment, adaptation won't work.  So, still, to justify induction
> as adaptation you have to make *some* assumptions about the world.
>
> The Occam prior gives one such assumption: that (to give just one form) sets
> of observations in the world tend to be producible by short computer
> programs.
>
> For adaptation to successfully carry out induction, *some* vaguely
> comparable property to this must hold, and I'm not sure if you have
> articulated which one you assume, or if you leave this open.
>
> In effect, you implicitly assume something like an Occam prior, because
> you're saying that  a system with finite resources can successfully adapt to
> the world ... which means that sets of observations in the world *must* be
> approximately summarizable via subprograms that can be executed within this
> system.
>
> So I argue that, even though it's not your preferred way to think about it,
> your own approach to AI theory and practice implicitly assumes some variant
> of the Occam prior holds in the real world.
>>
>>
>> Here I just want to point out that the original and basic meaning of
>> Occam's Razor and those two common (mis)usages of it are not
>> necessarily the same. I fully agree with the former, but not the
>> latter, and I haven't seen any convincing justification of the latter.
>> Instead, they are often taken as granted, under the name of Occam's
>> Razor.
>
> I agree that the notion of an Occam prior is a significant conceptual beyond
> the original "Occam's Razor" precept enounced long ago.
>
> Also, I note that, for those who posit the Occam prior as a **prior
> assumption**, there is not supposed to be any convincing justification for
> it.  The idea is simply that: one must make *some* assumption (explicitly or
> implicitly) if one wants to do induction, and this is the assumption that
> some people choose to make.
>
> -- Ben G
>
>
>
> ________________________________
> agi | Archives | Modify Your Subscription


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