Pei, Fully agree. The situation in mainstream AI is even worse on this
topic, compared to the new AGI community. Will you write something for AGI-08 on this?
Marcus suggested that I submit something to AGI-08. However I'm not sure what I could submit at the moment. I'll have a think about this after I've finished writing my thesis in a couple of months. if it searches different parts of the space in a context
and experience sensitive manner, it is intelligent; if it doesn't only search among listed alternatives, but also find out new alternatives, it is much more intelligent.
Hmmm. Ok, imagine that you have two optimization algorithms X and Y and they both solve some problem equally well. The difference is that Y uses twice as many resources as X to do it. As I understand your notion of intelligence, X would be considered more intelligent than Y. True? Essentially then, according to you intelligence depends on how well a system can perform per unit of resources consumed?
beside input/output of the system, you assume the rewards to be maximized come from the environment in a numerical form, which is an assumption not widely accepted outside the reinforcement learning community. For example, NARS may interpret certain input as reward, and certain other input as punishment, but it depends on many factors in the system, and is not objective at all. For this kind of systems (I'm sure NARS isn't the only one), how can your evaluation framework be applied?
NARS can... - accept a number as input? - be instructed to try to maximise this input? - interact with its environment in order to try to do this? I assume NARS is able to do all of these things. Shane ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415&user_secret=fabd7936
