On 2/27/08, Ben Goertzel <[EMAIL PROTECTED]> wrote:
> YKY<
>
> I thought you were   talking about the extraction of information that
> is explicitly stated in online text.
>
> Of course, inference is a separate process (though it may also play a
> role in direct information extraction).
>
> I don't think the rules of inference per se need to be learned.  In
> our book on PLN we outline a complete set of probabilistic logic
> inference rules, for example.
>
> What needs to be learned via experience is how to appropriately bias
> inference control -- how to sensibly prune the inference tree.
>
> So, one needs an inference engine that can adaptively learn better and
> better inference control as it carries out inferences.  We designed
> and partially implemented this feature in the NCE but never completed
> the work due to other priorities ... but I hope this can get done in
> NM or OpenCog sometime in late 2008..

I'm not talking about inference control here -- I assume that inference
control is done in a proper way, and there will still be a problem.  You
seem to assume that all knowledge = what is explicitly stated in online
texts.  So you deny that there is a large body of implicit knowledge other
than inference control rules (which are few in comparison).

I think that if your AGI doesn't have the implicit knowledge, it'd only be
able to perform simple inferences about statistical events -- for
example, calculating the probability of (lung cancer | smoking).

The kind of reasoning I'm interested in is more sophisticated.  For example,
I may ask the AGI to "open a file and print the 100th line" (in Java or C++,
say).  The AGI should be able to use a loop to read and discard the first 99
lines.  We need a step like:  "read 99 lines -> use a loop" but such a step
must be based on even simpler *concepts* of repetition and using loops.
What I'm saying is that your AGI does NOT have such rules and would be
incapable of thinking about such things.

YKY

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agi
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