I had similar feelings about William Pearson's recent message aboutI've been looking at something like this, but on a very generalized scale.
systems that use reinforcement learning:
>
> A reinforcement scenario, from wikipedia is defined as
>
> "Formally, the basic reinforcement learning model consists of:
>
> 1. a set of environment states S;
> 2. a set of actions A; and
> 3. a set of scalar "rewards" in the Reals.
> "
Here is my standard response to Behaviorism (which is what the above
reinforcement learning model actually is): Who decides when the rewards
should come, and who chooses what are the relevant "states" and "actions"?
If you find out what is doing *that* work, you have found your
intelligent system. And it will probably turn out to be so enormously
complex, relative to the reinforcement learning part shown above, that
the above formalism (assuming it has not been discarded by then) will be
almost irrelevant.
Just my deux centimes' worth.
Part 1 is infinite amount of states, incalculable,
Part 2 is a large huge amount, that can be broken into parts and (must be) dealt with.
Part 3 is what I am looking at currently.
Is it possible for us to generate a model calculation of Worth, or Happiness Value or GoodState value.
Just playing around with some ideas I have come up with:
GoodValue = alive * (a*health + b*wealth + c*enjoyment + d*learning + e*friends) +pastplans -time
This covers many simple motivations of humans currently, with a couple of these vague ideas that would need to be fleshed out. There is also a pastplans, which is a general preference for doing some things as patterns of past actions.
This is general right now, but would it be possible to flesh out a comlpex, changeable, but still sizeably managable equation here that could be a controlling factor on AI motivations?
James Ratcliff
Thank You
James Ratcliff
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