On Dec 6, 2007 8:06 PM, Ed Porter <[EMAIL PROTECTED]> wrote:
> Ben,
>
> To the extent it is not proprietary, could you please list some of the types
> of parameters that have to be tuned, and the types, if any, of
> Loosemore-type complexity problems you envision in Novamente or have
> experienced with WebMind, in such tuning and elsewhere?
>
> Ed Porter

A specific list of parameters would have no meaning without a huge
explanation which I don't have time to give...

Instead I'll list a few random areas where choices need to be made, that appear
localized at first but wind up affecting the whole

-- attention allocation is handled by an "artificial economy" mechanism, which
has the same sorts of parameters as any economic system (analogues of
interest rates,
rent rates, etc.)

-- program trees representing internal procedures are normalized via a set of
normalization rules, which collectively cast procedures into a certain
normal form.
There are many ways to do this.

-- the pruning of (backward and forward chaining) inference trees uses a
statistical "bandit problem" methodology, which requires a priori probabilities
to be ascribed to various inference steps


Fortunately though in each of the above three
examples there is theory that can guide parameter tning (different theories
in the three cases -- dynamic systems theory for the artificial economy; formal
computer science and language theory for program tree reduction; and Bayesian
stats for the pruning issue)

Webmind AI Engine had too many parameters and too much coupling between
subsystems.  We cast parameter optimization as an AI learning problem but it
was a hard one, though we did make headway on it.  Novamente Engine has much
coupling btw subsystems, but no unnecessary coupling; and many fewer
parameters on which system behavior can sensitively depend.  Definitely,
minimization of the number of needful-of-adjustment parameters is a very key
aspect of AGI system design.

-- Ben

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