On Sun, Jun 17, 2012 at 2:11 AM, Mike Tintner <[email protected]> wrote:
> How do you get to A): ?
>
>
> A)
> Two people in a big crowded space are unlikely to notice each other
>
> from:
>
> "Sue and Jane were both at the clinic at 4.00 - did they see each other?"
>
> How do you know to ask questions about the clinic and Sue and Jane and
> seeing?
>
> Please outline the **logical** principles  - esp. those you think existed in
> your head about "crowded spaces", "people" and "seeing."


If you knew more about real-world uses of logic systems, you would
know that **inference control** doesn't have to be done by logical
mechanisms....  The choice of which premises to explore in a logical
inference chain, can be done by lots of methods besides logic.  That
is, in a real-world reasoning context, logical inference will
generally be nudged and guided in the right direction by non-logical
methods...

In this case, a simple lookup into episodic memory would probably do
the trick...

If the system's memory contained many cases of people in the same
place who did see each other ,and also many cases of people in the
same place who did not see each other...

THEN, a supervised learning method like MOSES could be automatically
launched inside the system, to learn which patterns distinguish the
"did see" cases from the "didn't see" cases...

One of these patterns might be: if the people were in a place that is
both large and crowded, they often did not see each other...

This pattern, derived via inductive pattern-recognition from a set of
remembered instances, would then guide logical inference...

Note that a mind can try out 10000s of possible logical inferences
very quickly, in parallel, until it finds one that seems to yield
useful information about the subject at hand...

Using an internal simulation-world, as you suggest, would be one
possible way to solve the problem you mention.  However, there are
many other ways a mind could solve it, and I've described one:
uncertain logical inference, with inference control guided by
supervised learning acting on declarative episodic memory...

-- Ben G


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