Brad, Thanks for the encouragement.
For people who cannot fully grok the discussion from the email alone, the relevant NARS references are http://nars.wang.googlepages.com/wang.semantics.pdf and http://nars.wang.googlepages.com/wang.confidence.pdf Pei On Sat, Oct 11, 2008 at 1:13 AM, Brad Paulsen <[EMAIL PROTECTED]> wrote: > Pei, Ben G. and Abram, > > Oh, man, is this stuff GOOD! This is the real nitty-gritty of the AGI > matter. How does your approach handle counter-evidence? How does your > approach deal with insufficient evidence? (Those are rhetorical questions, > by the way -- I don't want to influence the course of this thread, just want > to let you know I dig it and, mostly, grok it as well). I love this stuff. > You guys are brilliant. Actually, I think it would make a good > publication: "PLN vs. NARS -- The AGI Smack-down!" A win-win contest. > > This is a rare treat for an old hacker like me. And, I hope, educational > for all (including the participants)! Keep it coming, please! > > Cheers, > Brad > > Pei Wang wrote: >> >> On Fri, Oct 10, 2008 at 8:03 PM, Ben Goertzel <[EMAIL PROTECTED]> wrote: >>> >>> Yah, according to Bayes rule if one assumes P(bird) = P(swimmer) this >>> would >>> be the case... >>> >>> (Of course, this kind of example is cognitively misleading, because if >>> the >>> only knowledge >>> the system has is "Swallows are birds" and "Swallows are NOT swimmers" >>> then >>> it doesn't >>> really know that the terms involved are "swallows", "birds", "swimmers" >>> etc. >>> ... then in >>> that case they're just almost-meaningless tokens to the system, right?) >> >> Well, it depends on the semantics. According to model-theoretic >> semantics, if a term has no reference, it has no meaning. According to >> experience-grounded semantics, every term in experience have meaning >> --- by the role it plays. >> >> Further questions: >> >> (1) Don't you intuitively feel that the evidence provided by >> non-swimming birds says more about "Birds are swimmers" than >> "Swimmers are birds"? >> >> (2) If your answer for (1) is "yes", then think about "Adults are >> alcohol-drinkers" and "Alcohol-drinkers are adults" --- do they have >> the same set of counter examples, intuitively speaking? >> >> (3) According to your previous explanation, will PLN also take a red >> apple as negative evidence for "Birds are swimmers" and "Swimmers are >> birds", because it reduces the "candidate pool" by one? Of course, the >> probability adjustment may be very small, but qualitatively, isn't it >> the same as a non-swimming bird? If not, then what the system will do >> about it? >> >> Pei >> >> >>> On Fri, Oct 10, 2008 at 7:34 PM, Pei Wang <[EMAIL PROTECTED]> wrote: >>>> >>>> Ben, >>>> >>>> I see your position. >>>> >>>> Let's go back to the example. If the only relevant domain knowledge >>>> PLN has is "Swallows are birds" and "Swallows are >>>> NOT swimmers", will the system assigns the same lower-than-default >>>> probability to "Birds are swimmers" and "Swimmers are birds"? Again, >>>> I only need a qualitative answer. >>>> >>>> Pei >>>> >>>> On Fri, Oct 10, 2008 at 7:24 PM, Ben Goertzel <[EMAIL PROTECTED]> wrote: >>>>> >>>>> Pei, >>>>> >>>>> I finally took a moment to actually read your email... >>>>> >>>>>> >>>>>> However, the negative evidence of one conclusion is no evidence of the >>>>>> other conclusion. For example, "Swallows are birds" and "Swallows are >>>>>> NOT swimmers" suggests "Birds are NOT swimmers", but says nothing >>>>>> about whether "Swimmers are birds". >>>>>> >>>>>> Now I wonder if PLN shows a similar asymmetry in induction/abduction >>>>>> on negative evidence. If it does, then how can that effect come out of >>>>>> a symmetric truth-function? If it doesn't, how can you justify the >>>>>> conclusion, which looks counter-intuitive? >>>>> >>>>> According to Bayes rule, >>>>> >>>>> P(bird | swimmer) P(swimmer) = P(swimmer | bird) P(bird) >>>>> >>>>> So, in PLN, evidence for P(bird | swimmer) will also count as evidence >>>>> for P(swimmer | bird), though potentially with a different weighting >>>>> attached to each piece of evidence >>>>> >>>>> If P(bird) = P(swimmer) is assumed, then each piece of evidence >>>>> for each of the two conditional probabilities, will count for the other >>>>> one symmetrically. >>>>> >>>>> The intuition here is the standard Bayesian one. >>>>> Suppose you know there >>>>> are 10000 things in the universe, and 1000 swimmers. >>>>> Then if you find out that swallows are not >>>>> swimmers ... then, unless you think there are zero swallows, >>>>> this does affect P(bird | swimmer). For instance, suppose >>>>> you think there are 10 swallows and 100 birds. Then, if you know for >>>>> sure >>>>> that swallows are not swimmers, and you have no other >>>>> info but the above, your estimate of P(bird|swimmer) >>>>> should decrease... because of the 1000 swimmers, you now know there >>>>> are only 990 that might be birds ... whereas before you thought >>>>> there were 1000 that might be birds. >>>>> >>>>> And the same sort of reasoning holds for **any** probability >>>>> distribution you place on the number of things in the universe, >>>>> the number of swimmers, the number of birds, the number of swallows. >>>>> It doesn't matter what assumption you make, whether you look at >>>>> n'th order pdf's or whatever ... the same reasoning works... >>>>> >>>>> From what I understand, your philosophical view is that it's somehow >>>>> wrong for a mind to make some assumption about the pdf underlying >>>>> the world around it? Is that correct? If so I don't agree with >>>>> this... >>>>> I >>>>> think this kind of assumption is just part of the "inductive bias" with >>>>> which >>>>> a mind approaches the world. >>>>> >>>>> The human mind may well have particular pdf's for stuff like birds and >>>>> trees wired into it, as we evolved to deal with these things. But >>>>> that's >>>>> not really the point. The inductive bias may be much more abstract -- >>>>> ultimately, it can just be an "occam bias" that biases the mind to >>>>> prior distributions (over the space of procedures for generating >>>>> prior distributions for handling specific cases) >>>>> that are simplest according to some wired-in >>>>> simplicity measure.... >>>>> >>>>> So again we get back to basic differences in philosophy... >>>>> >>>>> -- 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 >>> >>> >>> -- >>> Ben Goertzel, PhD >>> CEO, Novamente LLC and Biomind LLC >>> Director of Research, SIAI >>> [EMAIL PROTECTED] >>> >>> "Nothing will ever be attempted if all possible objections must be first >>> overcome " - Dr Samuel Johnson >>> >>> >>> ________________________________ >>> 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/?member_id=8660244&id_secret=114414975-3c8e69 Powered by Listbox: http://www.listbox.com
