Tuesday, February 11, 2003, 11:05:04 PM, Cliff Stabbert wrote: SL> However even within this scenario the concept of "fixed goal" is SL> something that we need to be careful about. The only real goal SL> of the AIXI system is to get as much reward as possible from its SL> environment. A "goal" is just our description of what that means. SL> If the AI gets reward for winning at chess then quickly it will get SL> very good at chess. If it then starts getting punished for winning SL> it will then quickly switch to losing at chess. Has the goal of SL> the system changed? Perhaps not. Perhaps the goal always was: SL> Win at chess up to point x in time and then switch to losing. SL> So we could say that the goal was always fixed, it's just that up SL> to point x in time the AI thought the goal was to alway win and it SL> wasn't until after point x in time that it realised that the real SL> goal was actually slightly more complex. In which case does it make SL> any sense to talk about AIXI as being limited by having fixed goals? SL> I think not.
I should add, the example you gave is what raised my questions: it seems to me an essentially untrainable case because it presents a *non-repeatable* scenario. If I were to give to an AGI a 1,000-page book, and on the first 672 pages was written the word "Not", it may predict that on the 673d page will be the word "Not.". But I could choose to make that page blank, and in that scenario, as in the above, I don't see how any algorithm, no matter how clever, could make that prediction (unless it included my realtime brainscans, etc.) -- Cliff ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
