Charles, The "computational complexity" or "resources expense" of NARS is another aspect on which this system is fundamentally different from existing systems. I understand that from the inference rules alone, people will think it is too expensive to be actually implemented, simply because there are so many possible ways to make inference. You may want to read http://nars.wang.googlepages.com/wang.computation.pdf to see how the inference processes are controlled in the system.
I've commented on the perceived limitation of the inheritance-based language in my comment on Edward. Pei On 10/8/07, Charles D Hixson <[EMAIL PROTECTED]> wrote: > Mike Tintner wrote: > > Vladimir: In experience-based learning there are two main problems > > relating to > >> knowledge acquisition: you have to come up with hypotheses and you > >> have to assess their plausibility. ...you create them based on various > >> heuristics. > > > > How is this different from narrow AI? It seems like narrow AI - does > > Nars have the ability to learn unprogrammed, or invent, totally new > > kinds of logic? Or kinds of algebra? > > > > In fact, the definitions of Nars: > > > > "NARS is "intelligent" in the sense that it is adaptive, and works > > with insufficient > > knowledge and resources. > > > > By "adaptive", we mean that NARS uses its experience (i.e., the > > history of its > > > > interaction with the environment) as the guidance of its inference > > activities. > > > > For each question, it looks for an answer that is most consistent with > > its > > > > experience (under the restriction of available resources)." > > > > define narrow AI systems - which are also "intelligent," "adaptive," > > "work with insufficient knowledge and resources" and learn from > > experience. There seems to be nothing in those definitions which is > > distinctive to AGI. > > > With a sufficient knowledge base, which would require learning, NARS > looks as if it could categorize that which it knows about, and make > guesses as to how certain pieces of information are related to other > pieces of information. > > An extended version should be "adaptive" in the patterns that it recognizes. > > OTOH, I don't recognize any features that would enable it to take > independent action, so I suspect that it would be but one module of a > more complex system. > N.B.: I'm definitely no expert at NARS, I've only read two of the > papers a a few arguments. Features that I didn't notice could well be > present. And they could certainly be in the planning stage. > > I'm a bit hesitant about the theoretical framework, as it appears > computationally expensive. Still, implementation doesn't necessarily > follow theory, and theory can jump over the gnarly bits, leaving them > for implementation. It's possible that lazy evaluation and postponed > stability calculations could make things a LOT more efficient. These > probably aren't practical until the database grows to a reasonable size, > however. > > But as I understand it, this still wouldn't be an AGI, but merely a > categorizer. (OTOH, I only read two of the papers. These could just be > the papers that cover the categorizer. Plausibly other papers cover > other aspects.) > > N.B.: The current version of NARS, as described, only parses a > specialized language covering topics of inheritance of characteristics. > As such, that's all that was covered by the paper I most recently read. > This doesn't appear to be an inherent limitation, as the terminal nodes > are primitive text and, as such, could, in principle, invoke other > routines, or refer to the contents of an image. The program would > neither know nor care. > > > ----- > 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/?&; > ----- 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=8660244&id_secret=51312250-bbbc49
