I'm not saying that the n-space approach wouldn't work, but I have used that approach before and faced a problem. It was because of that problem that I switched to a logic-based approach. Maybe you can solve it.
To illustrate it with an example, let's say the AGI can recognize apples, bananas, tables, chairs, the face of Einstein, etc, in the n-dimensional feature space. So, Einstein's face is defined by a hypersurface where each point is an instance of Einstein's face; and you can get a caricature of Einstein by going near the fringes of this hypervolume. So far so good. Now suppose you want to say: the apple is *on* the table, the banana is *on* the chair, etc. In logical form it would be on(table,apple), etc. There can be infinitely many such statements. The problem is that this thing, "on", is not definable in n-space via operations like AND, OR, NOT, etc. It seems that "on" is not definable by *any* hypersurface, so it cannot be learned by classifiers like feedforward neural networks or SVMs. You can define "apple on table" in n-space, which is the set of all configurations of apples on tables; but there is no way to define "X is on Y" as a hypervolume, and thus to make it learnable. This problem extends to other predicates besides on(x,y). YY ----- 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/?list_id=303
