I was trying to set up a simple ground rule case for a partial simulation
of AGI. As I started by thinking of the simplest case I could imagine and I
found that it was a little more interesting than I thought it would be.

I realized that the old numerical range could be used to test some
important ideas. The idea is that a set of narrow AI implementations could
be used to develop and test multiple possibility indexing. Suppose that the
program has learned various responses to thousands of situations. These
responses may be weighted for variations in kinds of cases. So in a typical
situation the program might detect hundreds of different characteristics
(in the observable input environment) that it had learned to associate with
some response so it would have to find strategies to choose the best
responses for the situation and strategies to learn from the experience.

Even if the situation - which is input to the simple AI program - is made
of distinct components, the possible combinations that might be relevant to
finding a good response could be very complex. For example, if it learned
abc represented a situation that it should respond to (with some kind of
response), it might wonder if abxc was a variant of that situation.

The problem here is managing and indexing of multiple possible responses
that were reasonable for a particular component-of-a-situation when the
typical situation might consist of hundreds of situation components.

So even a very simple AI simulation might lead to some interesting results.
(Of course there would have to be some way to evaluate the responses so the
simple simulation would either have to be tied to some game or it would
have to be worked out carefully).
Jim Bromer



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