[agi] Is anyone else here going to WORLDCOMP08?
Hi All, Is anyone else here going to WORLDCOMP08? That is in Las Vegas from July 14-17. It would sure be nice to discuss things at talking speed rather than typing speed. Steve Richfield --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
Re: [agi] WHAT SORT OF HARDWARE $33K AND $850K BUYS TODAY FOR USE IN AGI
Terren, Remember when I said that a purpose is not the same thing as a goal? The purpose that the system might be said to have embedded is attempting to maximise a certain signal. This purpose presupposes no ontology. The fact that this signal is attached to a human means the system as a whole might form the goal to try and please the human. Or depending on what the human does it might develop other goals. Goals are not the same as purposes. Goals require the intentional stance, purposes the design. To the extent that purpose is not related to goals, it is a meaningless term. In what possible sense is it worthwhile to talk about purpose if it doesn't somehow impact what an intelligent actually does? Does the following make sense? The purpose embedded within the system will be try and make the system not decrease in its ability to receive some abstract number. The way I connect up the abstract number to the real world will the govern what goals the system will likely develop (along with the initial programming). That is there is some connection, but it is tenuous and I don't have to specify an ontology. Will --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
[agi] need some help with loopy Bayes net
I'm considering nonmonotonic reasoning using Bayes net, and got stuck. There is an example on p483 of J Pearl's 1988 book PRIIS: Given: birds can fly penguins are birds penguins cannot fly The desiderata is to conclude that penguins are birds, but penguins cannot fly. Pearl translates the KB to: P(f | b) = high P(f | p) = low P(b | p) = high where high and low means arbitrarily close to 1 and 0, respectively. If you draw this on paper you'll see a triangular loop. Then Pearl continues to deduce: Conditioning P(f | p) on both b and ~b, P(f | p) = P(f | p,b) P(b | p) + P(f | p,~b) [1-P(b | p)] P(f | p,b) P(b | p) Thus P(f | p,b) P(f | p) / P(b | p) which is close to 0. Thus Pearl concludes that given penguin and bird, fly is not true. But I found something wrong here. It seems that the Bayes net is loopy and we can conclude that fly given penguin and bird can be either 0 or 1. (The loop is somewhat symmetric). Ben, do you have a similar problem dealing with nonmonotonicity using probabilistic networks? YKY --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
Re: [agi] need some help with loopy Bayes net
YKY, PLN, like NARS, uses inference trails Although we have tried omitting them, and found interesting results: errors do propagate, but not boundlessly, and network truth values are still meaningful Loopy Bayes nets basically just live with the circularity and rely on math properties of the Bayes net propagation rules to remove the possibility of error. Nice stuff, but it only works under fairly special assumptions. Traditional Bayes nets just assume a hierarchical structure and ignore the conditional probs not in accordance w/ the hierarchy, getting at them only indirectly via the ones in the hierarchy. This is why structure learning is so important in Bayes nets. -- Ben On Fri, Jul 4, 2008 at 4:10 AM, YKY (Yan King Yin) [EMAIL PROTECTED] wrote: I'm considering nonmonotonic reasoning using Bayes net, and got stuck. There is an example on p483 of J Pearl's 1988 book PRIIS: Given: birds can fly penguins are birds penguins cannot fly The desiderata is to conclude that penguins are birds, but penguins cannot fly. Pearl translates the KB to: P(f | b) = high P(f | p) = low P(b | p) = high where high and low means arbitrarily close to 1 and 0, respectively. If you draw this on paper you'll see a triangular loop. Then Pearl continues to deduce: Conditioning P(f | p) on both b and ~b, P(f | p) = P(f | p,b) P(b | p) + P(f | p,~b) [1-P(b | p)] P(f | p,b) P(b | p) Thus P(f | p,b) P(f | p) / P(b | p) which is close to 0. Thus Pearl concludes that given penguin and bird, fly is not true. But I found something wrong here. It seems that the Bayes net is loopy and we can conclude that fly given penguin and bird can be either 0 or 1. (The loop is somewhat symmetric). Ben, do you have a similar problem dealing with nonmonotonicity using probabilistic networks? YKY --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://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: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
Re: [agi] WHAT SORT OF HARDWARE $33K AND $850K BUYS TODAY FOR USE IN AGI
Will, --- On Fri, 7/4/08, William Pearson [EMAIL PROTECTED] wrote: Does the following make sense? The purpose embedded within the system will be try and make the system not decrease in its ability to receive some abstract number. The way I connect up the abstract number to the real world will the govern what goals the system will likely develop (along with the initial programming). That is there is some connection, but it is tenuous and I don't have to specify an ontology. Will I don't think I follow, but if I do, you're saying that the purpose of your system determines the goals of the system, which sounds like it's just semantics... Terren --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
Re: [agi] need some help with loopy Bayes net
Though there is a loop, YKY's problem not is caused by circular inference, but by multiple Inheritances, that is, different inference paths give different conclusions. This is indeed a problem in Bayes net, and there is no general solution in that theory, except in special cases. This problem is solved in NARS mainly by the confidence measurement, though inference trails are also relevant. See my Reference Classes and Multiple Inheritances at http://www.cogsci.indiana.edu/farg/peiwang/papers.html#reference_classes Pei On Fri, Jul 4, 2008 at 11:00 PM, Ben Goertzel [EMAIL PROTECTED] wrote: YKY, PLN, like NARS, uses inference trails Although we have tried omitting them, and found interesting results: errors do propagate, but not boundlessly, and network truth values are still meaningful Loopy Bayes nets basically just live with the circularity and rely on math properties of the Bayes net propagation rules to remove the possibility of error. Nice stuff, but it only works under fairly special assumptions. Traditional Bayes nets just assume a hierarchical structure and ignore the conditional probs not in accordance w/ the hierarchy, getting at them only indirectly via the ones in the hierarchy. This is why structure learning is so important in Bayes nets. -- Ben On Fri, Jul 4, 2008 at 4:10 AM, YKY (Yan King Yin) [EMAIL PROTECTED] wrote: I'm considering nonmonotonic reasoning using Bayes net, and got stuck. There is an example on p483 of J Pearl's 1988 book PRIIS: Given: birds can fly penguins are birds penguins cannot fly The desiderata is to conclude that penguins are birds, but penguins cannot fly. Pearl translates the KB to: P(f | b) = high P(f | p) = low P(b | p) = high where high and low means arbitrarily close to 1 and 0, respectively. If you draw this on paper you'll see a triangular loop. Then Pearl continues to deduce: Conditioning P(f | p) on both b and ~b, P(f | p) = P(f | p,b) P(b | p) + P(f | p,~b) [1-P(b | p)] P(f | p,b) P(b | p) Thus P(f | p,b) P(f | p) / P(b | p) which is close to 0. Thus Pearl concludes that given penguin and bird, fly is not true. But I found something wrong here. It seems that the Bayes net is loopy and we can conclude that fly given penguin and bird can be either 0 or 1. (The loop is somewhat symmetric). Ben, do you have a similar problem dealing with nonmonotonicity using probabilistic networks? YKY --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://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: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
Re: [agi] need some help with loopy Bayes net
YKY, I'm not certain this applies directly to your issue, but it's an interesting paper nonetheless: http://web.mit.edu/cocosci/Papers/nips00.ps. Cheers, Brad YKY (Yan King Yin) wrote: I'm considering nonmonotonic reasoning using Bayes net, and got stuck. There is an example on p483 of J Pearl's 1988 book PRIIS: Given: birds can fly penguins are birds penguins cannot fly The desiderata is to conclude that penguins are birds, but penguins cannot fly. Pearl translates the KB to: P(f | b) = high P(f | p) = low P(b | p) = high where high and low means arbitrarily close to 1 and 0, respectively. If you draw this on paper you'll see a triangular loop. Then Pearl continues to deduce: Conditioning P(f | p) on both b and ~b, P(f | p) = P(f | p,b) P(b | p) + P(f | p,~b) [1-P(b | p)] P(f | p,b) P(b | p) Thus P(f | p,b) P(f | p) / P(b | p) which is close to 0. Thus Pearl concludes that given penguin and bird, fly is not true. But I found something wrong here. It seems that the Bayes net is loopy and we can conclude that fly given penguin and bird can be either 0 or 1. (The loop is somewhat symmetric). Ben, do you have a similar problem dealing with nonmonotonicity using probabilistic networks? YKY --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
