Ed, Since NARS doesn't follow the Bayesian approach, there is no initial priors to be assumed. If we use a more general term, such as "initial knowledge" or "innate beliefs", then yes, you can add them into the system, will will improve the system's performance. However, they are optional. In NARS, all object-level (i.e., not meta-level) innate beliefs can be learned by the system afterward.
Pei On Tue, Oct 28, 2008 at 5:37 PM, Ed Porter <[EMAIL PROTECTED]> wrote: > It appears to me that the assumptions about initial priors used by a self > learning AGI or an evolutionary line of AGI's could be quite minimal. > > My understanding is that once a probability distribution starts receiving > random samples from its distribution the effect of the original prior > becomes rapidly lost, unless it is a rather rare one. Such rare problem > priors would get selected against quickly by evolution. Evolution would > tend to tune for the most appropriate priors for the success of subsequent > generations (either or computing in the same system if it is capable of > enough change or of descendant systems). Probably the best priors would > generally be ones that could be trained moderately rapidly by data. > > So it seems an evolutionary system or line could initially learn priors > without any assumptions for priors other than a random picking of priors. > Over time and multiple generations it might develop hereditary priors, an > perhaps even different hereditary priors for parts of its network connected > to different inputs, outputs or internal controls. > > The use of priors in an AGI could be greatly improved by having a gen/comp > hiearachy in which models for a given concept could be inherited from the > priors of sets of models for similar concepts, and that the set of priors > appropriate could change contextually. It would also seem that the notion > of a prior could be improve by blending information from episodic and > probabilistic models. > > It would appear than in almost any generally intelligent system, being able > to approximate reality in a manner sufficient for evolutionary success with > the most efficient representations would be a characteristic that would be > greatly preferred by evolution, because it would allow systems to better > model more of their environement sufficiently well for evolutionary success > with whatever current modeling capacity they have. > > So, although a completely accurate description of virtually anything may not > find much use for Occam's Razor, as a practically useful representation it > often will. It seems to me that Occam's Razor is more oriented to deriving > meaningful generalizations that it is exact descriptions of anything. > > Furthermore, it would seem to me that a more simple set of preconditions, is > generally more probable than a more complex one, because it requires less > coincidence. It would seem to me this would be true under most random sets > of priors for the probabilities of the possible sets of components involved > and Occam's Razor type selection. > > The are the musings of an untrained mind, since I have not spent much time > studying philosophy, because such a high percent of it was so obviously > stupid (such as what was commonly said when I was young, that you can't have > intelligence without language) and my understanding of math is much less > than that of many on this list. But none the less I think much of what I > have said above is true. > > I think its gist is not totally dissimilar to what Abram has said. > > Ed Porter > > > > > -----Original Message----- > From: Pei Wang [mailto:[EMAIL PROTECTED] > Sent: Tuesday, October 28, 2008 3:05 PM > To: agi@v2.listbox.com > Subject: Re: [agi] Occam's Razor and its abuse > > > Abram, > > I agree with your basic idea in the following, though I usually put it in > different form. > > Pei > > On Tue, Oct 28, 2008 at 2:52 PM, Abram Demski <[EMAIL PROTECTED]> wrote: >> Ben, >> >> You assert that Pei is forced to make an assumption about the >> regulatiry of the world to justify adaptation. Pei could also take a >> different argument. He could try to show that *if* a strategy exists >> that can be implemented given the finite resources, NARS will >> eventually find it. Thus, adaptation is justified on a sort of "we >> might as well try" basis. (The proof would involve showing that NARS >> searches the state of finite-state-machines that can be implemented >> with the resources at hand, and is more probable to stay for longer >> periods of time in configurations that give more reward, such that >> NARS would eventually settle on a configuration if that configuration >> consistently gave the highest reward.) >> >> So, some form of learning can take place with no assumptions. The >> problem is that the search space is exponential in the resources >> available, so there is some maximum point where the system would >> perform best (because the amount of resources match the problem), but >> giving the system more resources would hurt performance (because the >> system searches the unnecessarily large search space). So, in this >> sense, the system's behavior seems counterintuitive-- it does not seem >> to be taking advantage of the increased resources. >> >> I'm not claiming NARS would have that problem, of course.... just that >> a theoretical no-assumption learner would. >> >> --Abram >> >> On Tue, Oct 28, 2008 at 2:12 PM, Ben Goertzel <[EMAIL PROTECTED]> >> wrote: >>> >>> >>> On Tue, Oct 28, 2008 at 10:00 AM, Pei Wang <[EMAIL PROTECTED]> >>> wrote: >>>> >>>> Ben, >>>> >>>> Thanks. So the other people now see that I'm not attacking a straw >>>> man. >>>> >>>> My solution to Hume's problem, as embedded in the >>>> experience-grounded semantics, is to assume no predictability, but >>>> to justify induction as adaptation. However, it is a separate topic >>>> which I've explained in my other publications. >>> >>> Right, but justifying induction as adaptation only works if the >>> environment is assumed to have certain regularities which can be >>> adapted to. In a random environment, adaptation won't work. So, >>> still, to justify induction as adaptation you have to make *some* >>> assumptions about the world. >>> >>> The Occam prior gives one such assumption: that (to give just one >>> form) sets of observations in the world tend to be producible by >>> short computer programs. >>> >>> For adaptation to successfully carry out induction, *some* vaguely >>> comparable property to this must hold, and I'm not sure if you have >>> articulated which one you assume, or if you leave this open. >>> >>> In effect, you implicitly assume something like an Occam prior, >>> because you're saying that a system with finite resources can >>> successfully adapt to the world ... which means that sets of >>> observations in the world *must* be approximately summarizable via >>> subprograms that can be executed within this system. >>> >>> So I argue that, even though it's not your preferred way to think >>> about it, your own approach to AI theory and practice implicitly >>> assumes some variant of the Occam prior holds in the real world. >>>> >>>> >>>> Here I just want to point out that the original and basic meaning of >>>> Occam's Razor and those two common (mis)usages of it are not >>>> necessarily the same. I fully agree with the former, but not the >>>> latter, and I haven't seen any convincing justification of the >>>> latter. Instead, they are often taken as granted, under the name of >>>> Occam's Razor. >>> >>> I agree that the notion of an Occam prior is a significant conceptual >>> beyond the original "Occam's Razor" precept enounced long ago. >>> >>> Also, I note that, for those who posit the Occam prior as a **prior >>> assumption**, there is not supposed to be any convincing >>> justification for it. The idea is simply that: one must make *some* >>> assumption (explicitly or >>> implicitly) if one wants to do induction, and this is the assumption that >>> some people choose to make. >>> >>> -- Ben G >>> >>> >>> >>> ________________________________ >>> agi | Archives | Modify Your Subscription >> >> >> ------------------------------------------- >> agi >> Archives: https://www.listbox.com/member/archive/303/=now >> RSS Feed: https://www.listbox.com/member/archive/rss/303/ >> Modify Your Subscription: https://www.listbox.com/member/?&; Powered by >> Listbox: http://www.listbox.com >> > > > ------------------------------------------- > agi > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/ > Modify Your Subscription: > https://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com > > > > ------------------------------------------- > agi > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/ > Modify Your Subscription: https://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com > ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=117534816-b15a34 Powered by Listbox: http://www.listbox.com
