On Sunday 26 November 2006 14:14, Pei Wang wrote: > > In this design, the tough job is to make the agents working together > to cover all kinds of tasks, and for this part, I'm afraid that the > multi-dimensional space representation won't help much. Also, we > haven't seen much work on high-level cognition in that framework. > > Pei
and Richard in a similar vein: > > The problem with this, as I see it, is that the reason a physicist cares > about vector spaces is for their metrical properties: there is a > distance measure, and since that is the way the real world is, it buys > the physicist a *lot* of traction. But if you want to uses spaces for > this reason, I have to ask: why? What does the metricality of a space > buy you? And what makes you think that, in practice, you will actually > get what you wanted to get from it when you sit down and implement the > model? > Your mutual point is well taken, and I don't have a better reason than to note that a mouse and a giraffe have essentially the same skeleton -- evolution tends to take whatever works in one place and warp the hell out of it in another one, rather than come up with something new (and more optimal). It's something like me with my hammer and everything looking like a nail. My hammer in this case is that I know how to do a very straightforward memory-based learning within such a module, and I intend to have that, together with the interpolation and extrapolation capabilities I get for "free", at *every* point in the overall architecture, since it needs all the help it can get. But I really think that the metric properties of the spaces continue to help even at the very highest levels of abstraction. I'm willing to spend some time giving it a shot, anyway. So we'll see! Josh ----- 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
