Thursday, February 17, 2005
Goals and decision making
Keep in mind, that goal is not something ultimate. Goal is more like attraction point.
There could be several attraction points.
They shouldn't conflict with each other.
But they could compete with each other. Or quite contrary --- help to each other.
Goals provide direction of learning/self development.
> If the feedback is also NL then it's not very clear to me how you can
> increase understanding to the input.
Feedback could be in different form.
For instance, "satisfaction signal".
Another option --- NL. But special NL parser should be able to extract key words from NL and transform them into "satisfaction signal".
> other thing is that the AI is IMO not supposed to evaluate goals. It
> should be evaluating solutions.
Correct. AI should not evaluate hardcoded goals.
Hardcoded goals should evaluate feedback and make conclusions.
Not necessarily logical conclusions. More like emotional conclusions.
> I do not understand how you want to get complex problem solving
> working. That requires various types of reasoning.
I'm thinking about implementation of simple problem solving.
You are right --- complex problem solving requires more features.
I think basic features have to be implemented first.
Basic features would help to implement simple problem solving.
> Even if you combine ALL the words in all possible ways
Not in all possible combinations, but in "used combination".
> and if you have
> all that statistically sorted based on how often various combinations
> go together, it will be extremely poor problem solver because majority
> of solutions are just not based on that kind of statistical factor
Majority of humans' decisions are BASED on this statistical factor.
This majority consists of very simple problems/decisions though. (Like if I see "2 + 2" then I remember "4").
More complex decision making would be unavailable for this "statistical" approach. BUT(!) --- this "statistical" approach would help to quickly find limited set of possible solutions. And then more complex decision making algorithms would select right answer.
Keep in mind, that more complex algorithms are too slow and cannot solve the problem without simple "statistical" algorithm.
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There could be several attraction points.
They shouldn't conflict with each other.
But they could compete with each other. Or quite contrary --- help to each other.
Goals provide direction of learning/self development.
> If the feedback is also NL then it's not very clear to me how you can
> increase understanding to the input.
Feedback could be in different form.
For instance, "satisfaction signal".
Another option --- NL. But special NL parser should be able to extract key words from NL and transform them into "satisfaction signal".
> other thing is that the AI is IMO not supposed to evaluate goals. It
> should be evaluating solutions.
Correct. AI should not evaluate hardcoded goals.
Hardcoded goals should evaluate feedback and make conclusions.
Not necessarily logical conclusions. More like emotional conclusions.
> I do not understand how you want to get complex problem solving
> working. That requires various types of reasoning.
I'm thinking about implementation of simple problem solving.
You are right --- complex problem solving requires more features.
I think basic features have to be implemented first.
Basic features would help to implement simple problem solving.
> Even if you combine ALL the words in all possible ways
Not in all possible combinations, but in "used combination".
> and if you have
> all that statistically sorted based on how often various combinations
> go together, it will be extremely poor problem solver because majority
> of solutions are just not based on that kind of statistical factor
Majority of humans' decisions are BASED on this statistical factor.
This majority consists of very simple problems/decisions though. (Like if I see "2 + 2" then I remember "4").
More complex decision making would be unavailable for this "statistical" approach. BUT(!) --- this "statistical" approach would help to quickly find limited set of possible solutions. And then more complex decision making algorithms would select right answer.
Keep in mind, that more complex algorithms are too slow and cannot solve the problem without simple "statistical" algorithm.
NOTICE: This email is intended solely for the use of the individual to whom it is addressed and may contain information that is privileged, confidential or otherwise exempt from disclosure. If the reader of this email is not the intended recipient or the employee or agent responsible for delivering the message to the intended recipient, you are hereby notified that any dissemination, distribution, or copying of this communication is strictly prohibited. If you have received this communication in error, please immediately notify us by telephone and return the original message to us at the listed email address. Thank You.
