Re: [agi] OpenCog's logic compared to FOL?
One thing I don't get, YKY, is why you think you are going to take textbook methods that have already been shown to fail, and somehow make them work. Can't you see that many others have tried to use FOL and ILP already, and they've run into intractable combinatorial explosion problems? Some may argue that my approach isn't radical **enough** (and in spite of my innate inclination toward radicalism, I'm trying hard in my AGI work to be no more radical than is really needed, out of a desire to save time/ effort by reusing others' insights wherever possible) ... but at least I'm introducing a host of clearly novel technical ideas. What you seem to be suggesting is just to implement material from textbooks on a large knowledge base. Why do you think you're gonna make it work? Because you're gonna build a bigger KB than Cyc has built w/ their 20 years of effort and tens to hundreds of million of dollars of US gov't funding??? -- Ben G On Tue, Jun 3, 2008 at 3:46 PM, YKY (Yan King Yin) [EMAIL PROTECTED] wrote: Hi Ben, Note that I did not pick FOL as my starting point because I wanted to go against you, or be a troublemaker. I chose it because that's what the textbooks I read were using. There is nothing personal here. It's just like Chinese being my first language because I was born in China. I don't speak bad English just to sound different. I think the differences in our approaches are equally superficial. I don't think there is a compelling reason why your formalism is superior (or inferior, for that matter). You have domain-specific heuristics; I'm planning to have domain-specific heuristics too. The question really boils down to whether we should collaborate or not. And if we want meaningful collaboration, everyone must exert a little effort to make it happen. It cannot be one-way. 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] If men cease to believe that they will one day become gods then they will surely become worms. -- Henry Miller --- 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
Also, YKY, I can't help but note that your currently approach seems extremely similar to Texai (which seems quite similar to Cyc to me), more so than to OpenCog Prime (my proposal for a Novamente-like system built on OpenCog, not yet fully documented but I'm actively working on the docs now). I wonder why you don't join Stephen Reed on the texai project? Is it because you don't like the open-source nature of his project? ben On Tue, Jun 3, 2008 at 3:58 PM, Ben Goertzel [EMAIL PROTECTED] wrote: One thing I don't get, YKY, is why you think you are going to take textbook methods that have already been shown to fail, and somehow make them work. Can't you see that many others have tried to use FOL and ILP already, and they've run into intractable combinatorial explosion problems? Some may argue that my approach isn't radical **enough** (and in spite of my innate inclination toward radicalism, I'm trying hard in my AGI work to be no more radical than is really needed, out of a desire to save time/ effort by reusing others' insights wherever possible) ... but at least I'm introducing a host of clearly novel technical ideas. What you seem to be suggesting is just to implement material from textbooks on a large knowledge base. Why do you think you're gonna make it work? Because you're gonna build a bigger KB than Cyc has built w/ their 20 years of effort and tens to hundreds of million of dollars of US gov't funding??? -- Ben G On Tue, Jun 3, 2008 at 3:46 PM, YKY (Yan King Yin) [EMAIL PROTECTED] wrote: Hi Ben, Note that I did not pick FOL as my starting point because I wanted to go against you, or be a troublemaker. I chose it because that's what the textbooks I read were using. There is nothing personal here. It's just like Chinese being my first language because I was born in China. I don't speak bad English just to sound different. I think the differences in our approaches are equally superficial. I don't think there is a compelling reason why your formalism is superior (or inferior, for that matter). You have domain-specific heuristics; I'm planning to have domain-specific heuristics too. The question really boils down to whether we should collaborate or not. And if we want meaningful collaboration, everyone must exert a little effort to make it happen. It cannot be one-way. 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] If men cease to believe that they will one day become gods then they will surely become worms. -- Henry Miller -- Ben Goertzel, PhD CEO, Novamente LLC and Biomind LLC Director of Research, SIAI [EMAIL PROTECTED] If men cease to believe that they will one day become gods then they will surely become worms. -- Henry Miller --- 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
On 6/3/08, Ben Goertzel [EMAIL PROTECTED] wrote: Also, YKY, I can't help but note that your currently approach seems extremely similar to Texai (which seems quite similar to Cyc to me), more so than to OpenCog Prime (my proposal for a Novamente-like system built on OpenCog, not yet fully documented but I'm actively working on the docs now). I wonder why you don't join Stephen Reed on the texai project? Is it because you don't like the open-source nature of his project? You have built an AGI enterprise (at least, on the way to it). Often the *people* matter more than the technology. I *need* to collaborate with the community in order to win. And vice versa. Texai is closer to my theory but you have a bigger community. I don't have the resources to rebuild the infrastructure that you have, eg the virtual reality embodiment etc. Opensource is such a thorny issue. I don't have a clear idea yet... 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
Hi Ben, Note that I did not pick FOL as my starting point because I wanted to go against you, or be a troublemaker. I chose it because that's what the textbooks I read were using. There is nothing personal here. It's just like Chinese being my first language because I was born in China. I don't speak bad English just to sound different. I think the differences in our approaches are equally superficial. I don't think there is a compelling reason why your formalism is superior (or inferior, for that matter). You have domain-specific heuristics; I'm planning to have domain-specific heuristics too. The question really boils down to whether we should collaborate or not. And if we want meaningful collaboration, everyone must exert a little effort to make it happen. It cannot be one-way. 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
On 6/3/08, Ben Goertzel [EMAIL PROTECTED] wrote: 1) representing uncertainties in a way that leads to tractable, meaningful logical manipulations. Indefinite probabilities achieve this. I'm not saying they're the only way to achieve this, but I'll argue that single-number, Walley-interval, fuzzy, or full-pdf approaches are not adequate for various reasons. First of all, the *tractability* of your algorithm depends on heuristics that you design, which are separable from the underlying probabilistic logic calculus. In your mind, these 2 things may be mixed up. Indefinite probabilities DO NOT imply faster inference. Domain-specific heuristics do that. Secondly, I have no problem at all, with using your indefinite probability approach. It's a laudable achievement what you've accomplished. Thirdly, probabilistic logics -- of *any* flavor -- should [approximately] subsume binary logic if they are sound. So there is no reason why your logic is so different that it cannot be expressed in FOL. Fourthly, the approach that I'm more familiar with is interval probability. I acknowledge that you have gone further in this direction, and that's a good thing. 2) using inference rules that lead to relatively high-confidence uncertainty propagation. For instance term logic deduction is better for uncertain inference than modus ponens deduction, as detailed analysis reveals I believe term logic is translatable to FOL -- Fred Sommers mentioned that in his book. 3) propagating uncertainties meaningfully through abstract logical formulae involving nested quantifiers (we do this in a special way in PLN using third-order probabilities; I have not seen any other conceptually satisfactory solution) Again, that's well done. But are you saying that the same cannot be achieved using FOL? 4) most critically perhaps, using uncertain truth values within inference control to help pare down the combinatorial explosion Uncertain truth values DO NOT imply faster inference. In fact, they slow down inference wrt binary logic. If your inference algorithm is faster than resolution, and it's sound (so it subsumes binary logic), then you have found a faster FOL inference algorithm. But that's not true; what you're doing is domain-specific heuristics. How these questions are answered matters a LOT, and my colleagues and I spent years working on this stuff. It's not a matter of converting between equivalent formalisms. I think one can do indefinite probability + FOL + domain-specific heuristics just as you can do indefinite probability + term logic + domain-specific heuristics but it may cost an amount of effort that you're unwilling to pay. This is a very sad situation... 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
On 6/3/08, Ben Goertzel [EMAIL PROTECTED] wrote: One thing I don't get, YKY, is why you think you are going to take textbook methods that have already been shown to fail, and somehow make them work. Can't you see that many others have tried to use FOL and ILP already, and they've run into intractable combinatorial explosion problems? Calm down =) I'll use domain-specific heuristics just as you do. There's nothing wrong with textbooks. Some may argue that my approach isn't radical **enough** (and in spite of my innate inclination toward radicalism, I'm trying hard in my AGI work to be no more radical than is really needed, out of a desire to save time/ effort by reusing others' insights wherever possible) ... but at least I'm introducing a host of clearly novel technical ideas. Yes, I acknowledge that you have novel ideas. But do you really think I'm so dumb that I ONLY use textbook ideas? I try to integrate existing methods. My style of innovation is kind of subtle. You have done something new, but not so new as to be in a totally different dimension. 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
As we have discussed a while back on the OpenCog mail list, I would like to see a RDF interface to some level of the OpenCog Atom Table. I think that would suit both YKY and myself. Our discussion went so far as to consider ways to assign URI's to appropriate atoms. Yes, I still think that's a good idea and I'm fairly sure it will happen this year... probably not too long after the code is considered really ready for release... ben --- 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
First of all, the *tractability* of your algorithm depends on heuristics that you design, which are separable from the underlying probabilistic logic calculus. In your mind, these 2 things may be mixed up. Indefinite probabilities DO NOT imply faster inference. Domain-specific heuristics do that. Not all heuristics for inference control are narrowly domain-specific Some may be generally applicable across very broad sets of domains, say across all domains satisfying certain broad mathematical properties such as similar theorems tend to have similar proofs. So, I agree that indefinite probabilities themselves don't imply faster inference. However, we have some heuristics for (relatively) fast inference control that we believe will apply across any domains satisfying certain broad mathematical properties ... and that won't work with traditional representations of uncertainty Secondly, I have no problem at all, with using your indefinite probability approach. It's a laudable achievement what you've accomplished. Thirdly, probabilistic logics -- of *any* flavor -- should [approximately] subsume binary logic if they are sound. So there is no reason why your logic is so different that it cannot be expressed in FOL. Yes of course it can be expressed in FOL ... it can be expressed in Morse Code too, but I don't see a point to it ;-) ... it could also be realized via a mechanical contraption made of TinkerToys ... like Danny Hillis's http://www.ohgizmo.com/wp-content/uploads/2006/12/tinkertoycomputer_1.jpg ;-) But are you saying that the same cannot be achieved using FOL? If you attach indefinite probabilities to FOL propositions, and create indefinite probability formulas corresponding to standard FOL rules, you will have a subset of PLN But you'll have a hard time applying Bayes rule to FOL propositions without being willing to assign probabilities to terms ... and you'll have a hard time applying it to FOL variable expressions without doing something that equates to assigning probabilities to propositions w. unbound variables ... and like I said, I haven't seen any other adequate way of propagating pdf's through quantifiers than the one we use in PLN, though Halpern's book describes a lot of inadequate ways ;-) 4) most critically perhaps, using uncertain truth values within inference control to help pare down the combinatorial explosion Uncertain truth values DO NOT imply faster inference. In fact, they slow down inference wrt binary logic. If your inference algorithm is faster than resolution, and it's sound (so it subsumes binary logic), then you have found a faster FOL inference algorithm. But that's not true; what you're doing is domain-specific heuristics. As noted above, the truth is somewhere inbetween. You can find inference control heuristics that exploit general mathematical properties of domains -- so they don't apply to ALL domains, but nor are they specialized to any particular domain. Evolution is like this in fact -- it's no good at optimizing random fitness functions, but it's good at optimizing fitness functions satisfying certain mathematical properties, regardless of the specific domain they refer to I think one can do indefinite probability + FOL + domain-specific heuristics just as you can do indefinite probability + term logic + domain-specific heuristics but it may cost an amount of effort that you're unwilling to pay. well we do both in PLN ... PLN is not a pure term logic... This is a very sad situation... Oh ... I thought it was funny ... I suppose I'm glad I have a perverse sense of humour ;-D ben --- 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
Hi Ben. Thanks for suggesting that YKY collaborate with Texai because of our similar approaches to knowledge representation. I believe that Cyc's lack of AGI progress is not due to their choice of FOL but rather that Cycorp emphasizes the hand-crafting of commonsense knowledge about things while disfavoring skill acquisition. Texai will test the hypothesis that Cyc-style FOL (i.e. a RDF compatible subset) can represent procedures sufficient to support a mechanism that learns knowledge and skills, by being taught by mentors using natural language. My initial bootstrap subject domain choices are: * lexicon acquisition (e.g. mapping WordNet synsets to OpenCyc-style terms) * grammar rule acquisition * Java program synthesis - to support skill acquisition and executionI believe that the crisp (i.e. certain or very near certain) KR for these domains will facilitate the use of FOL inference (e.g. subsumption) when I need it to supplement the current Texai spreading activation techniques for word sense disambiguation and relevance reasoning. I expect that OpenCog will focus on domains that require probabilistic reasoning, e.g. pattern recognition, which I am postponing until Texai is far enough along that expert mentors can teach it the skills for probabilistic reasoning. --- As we have discussed a while back on the OpenCog mail list, I would like to see a RDF interface to some level of the OpenCog Atom Table. I think that would suit both YKY and myself. Our discussion went so far as to consider ways to assign URI's to appropriate atoms. Cheers, -Steve Stephen L. Reed Artificial Intelligence Researcher http://texai.org/blog http://texai.org 3008 Oak Crest Ave. Austin, Texas, USA 78704 512.791.7860 - Original Message From: Ben Goertzel [EMAIL PROTECTED] To: agi@v2.listbox.com Sent: Tuesday, June 3, 2008 1:59:54 AM Subject: Re: [agi] OpenCog's logic compared to FOL? Also, YKY, I can't help but note that your currently approach seems extremely similar to Texai (which seems quite similar to Cyc to me), more so than to OpenCog Prime (my proposal for a Novamente-like system built on OpenCog, not yet fully documented but I'm actively working on the docs now). I wonder why you don't join Stephen Reed on the texai project? Is it because you don't like the open-source nature of his project? ben On Tue, Jun 3, 2008 at 3:58 PM, Ben Goertzel [EMAIL PROTECTED] wrote: One thing I don't get, YKY, is why you think you are going to take textbook methods that have already been shown to fail, and somehow make them work. Can't you see that many others have tried to use FOL and ILP already, and they've run into intractable combinatorial explosion problems? Some may argue that my approach isn't radical **enough** (and in spite of my innate inclination toward radicalism, I'm trying hard in my AGI work to be no more radical than is really needed, out of a desire to save time/ effort by reusing others' insights wherever possible) ... but at least I'm introducing a host of clearly novel technical ideas. What you seem to be suggesting is just to implement material from textbooks on a large knowledge base. Why do you think you're gonna make it work? Because you're gonna build a bigger KB than Cyc has built w/ their 20 years of effort and tens to hundreds of million of dollars of US gov't funding??? -- Ben G On Tue, Jun 3, 2008 at 3:46 PM, YKY (Yan King Yin) [EMAIL PROTECTED] wrote: Hi Ben, Note that I did not pick FOL as my starting point because I wanted to go against you, or be a troublemaker. I chose it because that's what the textbooks I read were using. There is nothing personal here. It's just like Chinese being my first language because I was born in China. I don't speak bad English just to sound different. I think the differences in our approaches are equally superficial. I don't think there is a compelling reason why your formalism is superior (or inferior, for that matter). You have domain-specific heuristics; I'm planning to have domain-specific heuristics too. The question really boils down to whether we should collaborate or not. And if we want meaningful collaboration, everyone must exert a little effort to make it happen. It cannot be one-way. 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] If men cease to believe that they will one day become gods then they will surely become worms. -- Henry Miller -- Ben Goertzel, PhD CEO, Novamente LLC and Biomind LLC Director of Research, SIAI [EMAIL PROTECTED] If men cease to believe that they will one
Re: [agi] OpenCog's logic compared to FOL?
You have done something new, but not so new as to be in a totally different dimension. YKY I have some ideas more like that too but I've postponed trying to sell them to others, for the moment ;-) ... it's hard enough to sell fairly basic stuff like PLN ... Look for some stuff on the applications of hypersets and division algebras to endowing AGIs with free will and reflective awareness, maybe in early 09 ... ;) -- Ben --- 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
Ben,
If we don't work out the correspondence (even approximately) between
FOL and term logic, this conversation would not be very fruitful. I
don't even know what you're doing with PLN. I suggest we try to work
it out here step by step. If your approach really makes sense to me,
you will gain another helper =) Also, this will be good for your
project's documentation.
I have some examples:
Eng: Some philosophers are wise
TL: +Philosopher+Wise
FOL: philosopher(X) - wise(X)
Eng: Romeo loves Juliet
TL: +-Romeo* + (Loves +-Juliet*)
FOL: loves(romeo, juliet)
Eng: Women often have long hair
TL: ?
FOL: woman(X) - long_hair(X)
I know your term logic is slightly different from Fred Sommers'. Can
you fill in the TL parts and also attach indefinite probabilities?
On 6/3/08, Ben Goertzel [EMAIL PROTECTED] wrote:
If you attach indefinite probabilities to FOL propositions, and create
indefinite probability formulas corresponding to standard FOL rules,
you will have a subset of PLN
But you'll have a hard time applying Bayes rule to FOL propositions
without being willing to assign probabilities to terms ... and you'll
have a hard time applying it to FOL variable expressions without doing
something that equates to assigning probabilities to propositions w.
unbound variables ... and like I said, I haven't seen any other
adequate way of propagating pdf's through quantifiers than the one we
use in PLN, though Halpern's book describes a lot of inadequate ways
;-)
Re assigning probabilties to terms...
Term in term logic is completely different from term in FOL. I
guess terms in term logic roughly correspond to predicates or
propositions in FOL. Terms in FOL seem to have no counterpart in term
logic..
Anyway there should be no confusion here. Propositions are the ONLY
things that can have truth values. This applies to term logic as well
(I just refreshed my memory of TL). When truth values go from { 0, 1
} to [ 0, 1 ], we get single-value probabilistic logic. All this has
a very solid and rigorous foundation, based on so-called model theory.
YKY
---
agi
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Re: [agi] OpenCog's logic compared to FOL?
Propositions are not the only things that can have truth values...
I don't have time to carry out a detailed mathematical discussion of
this right now...
We're about to (this week) finalize the PLN book draft ... I'll send
you a pre-publication PDF early next week and then you can read it and
we can argue this stuff after that ;-)
ben
On Wed, Jun 4, 2008 at 1:01 AM, YKY (Yan King Yin)
[EMAIL PROTECTED] wrote:
Ben,
If we don't work out the correspondence (even approximately) between
FOL and term logic, this conversation would not be very fruitful. I
don't even know what you're doing with PLN. I suggest we try to work
it out here step by step. If your approach really makes sense to me,
you will gain another helper =) Also, this will be good for your
project's documentation.
I have some examples:
Eng: Some philosophers are wise
TL: +Philosopher+Wise
FOL: philosopher(X) - wise(X)
Eng: Romeo loves Juliet
TL: +-Romeo* + (Loves +-Juliet*)
FOL: loves(romeo, juliet)
Eng: Women often have long hair
TL: ?
FOL: woman(X) - long_hair(X)
I know your term logic is slightly different from Fred Sommers'. Can
you fill in the TL parts and also attach indefinite probabilities?
On 6/3/08, Ben Goertzel [EMAIL PROTECTED] wrote:
If you attach indefinite probabilities to FOL propositions, and create
indefinite probability formulas corresponding to standard FOL rules,
you will have a subset of PLN
But you'll have a hard time applying Bayes rule to FOL propositions
without being willing to assign probabilities to terms ... and you'll
have a hard time applying it to FOL variable expressions without doing
something that equates to assigning probabilities to propositions w.
unbound variables ... and like I said, I haven't seen any other
adequate way of propagating pdf's through quantifiers than the one we
use in PLN, though Halpern's book describes a lot of inadequate ways
;-)
Re assigning probabilties to terms...
Term in term logic is completely different from term in FOL. I
guess terms in term logic roughly correspond to predicates or
propositions in FOL. Terms in FOL seem to have no counterpart in term
logic..
Anyway there should be no confusion here. Propositions are the ONLY
things that can have truth values. This applies to term logic as well
(I just refreshed my memory of TL). When truth values go from { 0, 1
} to [ 0, 1 ], we get single-value probabilistic logic. All this has
a very solid and rigorous foundation, based on so-called model theory.
YKY
---
agi
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--
Ben Goertzel, PhD
CEO, Novamente LLC and Biomind LLC
Director of Research, SIAI
[EMAIL PROTECTED]
If men cease to believe that they will one day become gods then they
will surely become worms.
-- Henry Miller
---
agi
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Re: [agi] OpenCog's logic compared to FOL?
On 6/4/08, Ben Goertzel [EMAIL PROTECTED] wrote: Propositions are not the only things that can have truth values... Terms in term logic can have truth values. But such terms correspond to propositions in FOL. There is absolutely no confusion here. I don't have time to carry out a detailed mathematical discussion of this right now... We're about to (this week) finalize the PLN book draft ... I'll send you a pre-publication PDF early next week and then you can read it and we can argue this stuff after that ;-) Thanks alot =) 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re : [agi] OpenCog's logic compared to FOL?
hello ben
if i can have a pdf draf,i think you very much
bruno
- Message d'origine
De : Ben Goertzel [EMAIL PROTECTED]
À : agi@v2.listbox.com
Envoyé le : Mardi, 3 Juin 2008, 18h33mn 02s
Objet : Re: [agi] OpenCog's logic compared to FOL?
Propositions are not the only things that can have truth values...
I don't have time to carry out a detailed mathematical discussion of
this right now...
We're about to (this week) finalize the PLN book draft ... I'll send
you a pre-publication PDF early next week and then you can read it and
we can argue this stuff after that ;-)
ben
On Wed, Jun 4, 2008 at 1:01 AM, YKY (Yan King Yin)
[EMAIL PROTECTED] wrote:
Ben,
If we don't work out the correspondence (even approximately) between
FOL and term logic, this conversation would not be very fruitful. I
don't even know what you're doing with PLN. I suggest we try to work
it out here step by step. If your approach really makes sense to me,
you will gain another helper =) Also, this will be good for your
project's documentation.
I have some examples:
Eng: Some philosophers are wise
TL: +Philosopher+Wise
FOL: philosopher(X) - wise(X)
Eng: Romeo loves Juliet
TL: +-Romeo* + (Loves +-Juliet*)
FOL: loves(romeo, juliet)
Eng: Women often have long hair
TL: ?
FOL: woman(X) - long_hair(X)
I know your term logic is slightly different from Fred Sommers'. Can
you fill in the TL parts and also attach indefinite probabilities?
On 6/3/08, Ben Goertzel [EMAIL PROTECTED] wrote:
If you attach indefinite probabilities to FOL propositions, and create
indefinite probability formulas corresponding to standard FOL rules,
you will have a subset of PLN
But you'll have a hard time applying Bayes rule to FOL propositions
without being willing to assign probabilities to terms ... and you'll
have a hard time applying it to FOL variable expressions without doing
something that equates to assigning probabilities to propositions w.
unbound variables ... and like I said, I haven't seen any other
adequate way of propagating pdf's through quantifiers than the one we
use in PLN, though Halpern's book describes a lot of inadequate ways
;-)
Re assigning probabilties to terms...
Term in term logic is completely different from term in FOL. I
guess terms in term logic roughly correspond to predicates or
propositions in FOL. Terms in FOL seem to have no counterpart in term
logic..
Anyway there should be no confusion here. Propositions are the ONLY
things that can have truth values. This applies to term logic as well
(I just refreshed my memory of TL). When truth values go from { 0, 1
} to [ 0, 1 ], we get single-value probabilistic logic. All this has
a very solid and rigorous foundation, based on so-called model theory.
YKY
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Re: [agi] OpenCog's logic compared to FOL?
On 6/3/08, Stephen Reed [EMAIL PROTECTED] wrote: I believe that the crisp (i.e. certain or very near certain) KR for these domains will facilitate the use of FOL inference (e.g. subsumption) when I need it to supplement the current Texai spreading activation techniques for word sense disambiguation and relevance reasoning. I expect that OpenCog will focus on domains that require probabilistic reasoning, e.g. pattern recognition, which I am postponing until Texai is far enough along that expert mentors can teach it the skills for probabilistic reasoning. Your approach is sensible, indeed similar to mine -- I'm also experimenting with crisp logic only. But there are 2 problems: 1. Probabilistic inference cannot be grafted onto crisp logic easily. The changes may be so great that much of the original work will be rendered useless. 2. You think we can do program synthesis with crisp logic only? This has profound implications if true... 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
On 6/3/08, Matt Mahoney [EMAIL PROTECTED] wrote: Do you have any insights on how this learning will be done? That research area is known as ILP (inductive logic programming). It's very powerful in the sense that almost anything (eg, any Prolog program) can be learned that way. But the problem is that the combinatorial explosion is so great that you must use heuristics and biases. So far no one has applied it to large-scale commonsense learning. Some Cyc people have experimented with it recently. Cyc put a lot of effort into a natural language interface and failed. What approach will you use that they have not tried? FOL requires a set of transforms, e.g. All men are mortal - forall X, man(X) - mortal(X) (hard) Socrates is a man - (man(Socrates) (hard) - mortal(Socrates) (easy) - Socrates is mortal (hard). We have known for a long time how to solve the easy parts. The hard parts are AI-complete. You have to solve AI before you can learn the knowledge base. Then after you build it, you won't need it. What is the point? We don't need 100% perfect NLP ability to learn the KB. An NL interface that can accept a simple subset of English will do. 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
YKY said: 1. Probabilistic inference cannot be grafted onto crisp logic easily. The changes may be so great that much of the original work will be rendered useless. Agreed. However, I hope that by the time probabilistic inference is taught to Texai by mentors, it will be easy to supersede useless skills with correct ones. 2. You think we can do program synthesis with crisp logic only? This has profound implications if true... All of the work to date on program generation, macro processing, application configuration via parameters, compilation, assembly, and program optimization has used crisp knowledge representation (i.e. non-probabilistic data structures). Dynamic, feedback based optimizing compilers, such as the Java HotSpot VM, do keep track of program path statistics in order to decide when to inline methods for example. But on the whole, the traditional program development life cycle is free of probabilistic inference. I have a hypothesis that program design (to satisfy requirements), and in general engineering design, can be performed using crisp knowledge representation - with the provision that I will use cognitively-plausible spreading activation instead of, or to cache, time-consuming deductive backchaining. My current work will explore this hypothesis with regard to composing simple programs that compose skills from more primitive skills. I am adapting Gerhard Wickler's Capability Description Language to match capabilities (e.g. program composition capabilities) with tasks (e.g. clear a StringBuilder object). CDL conveniently uses a crisp FOL knowledge representation. Here is a Texai behavior language file that contains capability descriptions for primitive Java compositions. Each of these primitive capabilities is implemented by a Java object that can be persisted in the Texai KB as RDF statements. Like yourself, I find the profound implications of automatic programming fascinating. I can only hope that this fascination has guided me down the right path to AGI, rather than down a dead end. I've written a brief blog post on this and related AI-hard problems. Cheers. -Steve Stephen L. Reed Artificial Intelligence Researcher http://texai.org/blog http://texai.org 3008 Oak Crest Ave. Austin, Texas, USA 78704 512.791.7860 - Original Message From: YKY (Yan King Yin) [EMAIL PROTECTED] To: agi@v2.listbox.com Sent: Tuesday, June 3, 2008 12:20:19 PM Subject: Re: [agi] OpenCog's logic compared to FOL? On 6/3/08, Stephen Reed [EMAIL PROTECTED] wrote: I believe that the crisp (i.e. certain or very near certain) KR for these domains will facilitate the use of FOL inference (e.g. subsumption) when I need it to supplement the current Texai spreading activation techniques for word sense disambiguation and relevance reasoning. I expect that OpenCog will focus on domains that require probabilistic reasoning, e.g. pattern recognition, which I am postponing until Texai is far enough along that expert mentors can teach it the skills for probabilistic reasoning. Your approach is sensible, indeed similar to mine -- I'm also experimenting with crisp logic only. But there are 2 problems: 1. Probabilistic inference cannot be grafted onto crisp logic easily. The changes may be so great that much of the original work will be rendered useless. 2. You think we can do program synthesis with crisp logic only? This has profound implications if true... YKY agi | Archives | Modify Your Subscription --- 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
On 6/4/08, Stephen Reed [EMAIL PROTECTED] wrote: All of the work to date on program generation, macro processing, application configuration via parameters, compilation, assembly, and program optimization has used crisp knowledge representation (i.e. non-probabilistic data structures). Dynamic, feedback based optimizing compilers, such as the Java HotSpot VM, do keep track of program path statistics in order to decide when to inline methods for example. But on the whole, the traditional program development life cycle is free of probabilistic inference. How about these scenarios: 1. If a task is to be repeated 'many' times, use a loop. If only 'a few' times, write it out directly. -- this requires fuzziness 2. The gain of using algorihtm X on this problem is likely to be small. -- requires probability I have a hypothesis that program design (to satisfy requirements), and in general engineering design, can be performed using crisp knowledge representation - with the provision that I will use cognitively-plausible spreading activation instead of, or to cache, time-consuming deductive backchaining. My current work will explore this hypothesis with regard to composing simple programs that compose skills from more primitive skills. I am adapting Gerhard Wickler's Capability Description Languagehttp://www.aiai.ed.ac.uk/oplan/cdl/index.htmlto match capabilities (e.g. program composition capabilities) with tasks (e.g. clear a StringBuilder object). CDL conveniently uses a crisp FOL knowledge representation. Herehttp://texai.svn.sourceforge.net/viewvc/texai/BehaviorLanguage/data/method-definitions.bl?view=markupis a Texai behavior language file that contains capability descriptions for primitive Java compositions. Each of these primitive capabilities is implemented by a Java object that can be persisted in the Texai KB as RDF statements. Maybe you mean spreading activation is used to locate candidate facts / rules, over which actual deductions are attempted? That sounds very promising. One question is how to learn the association between nodes. 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
YKY said: How about these scenarios: 1. If a task is to be repeated 'many' times, use a loop. If only 'a few' times, write it out directly. -- this requires fuzziness 2. The gain of using algorithm X on this problem is likely to be small. -- requires probability Agreed. When Texai gets to this point I would incorporate an open source fuzzy logic library such as JFuzzyLogic. I believe I can interface the Texai KB to a fuzzy logic library without too much difficulty. Maybe you mean spreading activation is used to locate candidate facts / rules, over which actual deductions are attempted? That sounds very promising. One question is how to learn the association between nodes. To be clear, I would do the opposite. Offline backchaining, deductive inference could be performed to cache conclusions for common inference problems. The cache is implemented via spreading activation links between the antecedent terms of the rules and the consequent terms of the conclusions. Humans do not perform modus ponens deduction from first principles for commonsense problem solving. I believe that spreading activation can be employed to perform machine problem solving (e.g. executing a learned procedure) in a cognitively plausible fashion without real-time theorem proving. Cheers. -Steve Stephen L. Reed Artificial Intelligence Researcher http://texai.org/blog http://texai.org 3008 Oak Crest Ave. Austin, Texas, USA 78704 512.791.7860 - Original Message From: YKY (Yan King Yin) [EMAIL PROTECTED] To: agi@v2.listbox.com Sent: Tuesday, June 3, 2008 5:29:07 PM Subject: Re: [agi] OpenCog's logic compared to FOL? On 6/4/08, Stephen Reed [EMAIL PROTECTED] wrote: All of the work to date on program generation, macro processing, application configuration via parameters, compilation, assembly, and program optimization has used crisp knowledge representation (i.e. non-probabilistic data structures). Dynamic, feedback based optimizing compilers, such as the Java HotSpot VM, do keep track of program path statistics in order to decide when to inline methods for example. But on the whole, the traditional program development life cycle is free of probabilistic inference. How about these scenarios: 1. If a task is to be repeated 'many' times, use a loop. If only 'a few' times, write it out directly. -- this requires fuzziness 2. The gain of using algorihtm X on this problem is likely to be small. -- requires probability I have a hypothesis that program design (to satisfy requirements), and in general engineering design, can be performed using crisp knowledge representation - with the provision that I will use cognitively-plausible spreading activation instead of, or to cache, time-consuming deductive backchaining. My current work will explore this hypothesis with regard to composing simple programs that compose skills from more primitive skills. I am adapting Gerhard Wickler's Capability Description Language to match capabilities (e.g. program composition capabilities) with tasks (e.g. clear a StringBuilder object). CDL conveniently uses a crisp FOL knowledge representation. Here is a Texai behavior language file that contains capability descriptions for primitive Java compositions. Each of these primitive capabilities is implemented by a Java object that can be persisted in the Texai KB as RDF statements. Maybe you mean spreading activation is used to locate candidate facts / rules, over which actual deductions are attempted? That sounds very promising. One question is how to learn the association between nodes. YKY agi | Archives | Modify Your Subscription --- 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
I think it's fine that you use the term atom in your own way. The important thing is, whatever the objects that you attach probabilities to, that class of objects should correspond to *propositions* in FOL. From there it would be easier for me to understand your ideas. Well, no, we attach probabilities to terms as well as to relationships ... and to expressions with free as well as bound variables... You can map terms and free-variable expressions into propositions if you want to, though... for instance the term cat has probability P(cat) which you could interpret as P(x is a cat | x is in my experience base) and the free-variable expression eats(x, mouse) has probability P( eats(x,mouse) ) which can be interpreted as P( eats(x,mouse) is true | x is in my experience base) However these propositional representations are a bit awkward and are not the way to represent things for the PLN rules to be simply applied... it is nicer by far to leave the experiential semantics implicit... -- Ben G --- 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
On 6/2/08, Ben Goertzel [EMAIL PROTECTED] wrote: eats(x, mouse) That's a perfectly legitimate proposition. So it is perfectly OK to write: P( eats(x,mouse) ) Note here that I assume your mouse refers to a particular instance of a mouse, as in: eats(X, mouse_1234) What's confusing is: for instance the term cat has probability P(cat) P(x is a cat | x is in my experience base) In FOL, the term term means either a constant, a variable, or a function applied to a tuple of other terms. In other words, terms in FOL are objects, not propositions. Examples of terms in FOL: stray_cat_1234 mary_queen_of_scots X mother(X) mother(mary_queen_of_scots) etc... If you want to say P ( X is a cat | X is in my experience base ) the corresponding FOL proposition should be: cat(X) instead of cat. I think your notation of cat translates to cat(X) in FOL. Your experience base may contain an instances such as: cat( stray_cat_1234 ) female( mary_queen_of_scots ) eats( cat_4567, mouse_890 ) etc... You can map terms and free-variable expressions into propositions if you want to, though... It's a bit confusing to map OpenCog terms to FOL propositions. IMO terms should not have probabilities attached to them. Anyway let me just leave that decision to you. No more comments. However these propositional representations are a bit awkward and are not the way to represent things for the PLN rules to be simply applied... it is nicer by far to leave the experiential semantics implicit... I'm interested to see how this is done. 1. The contents of your experience base can be translated to FOL. 2. Reasoning algorithms in FOL such as resolution are known to be quite complex and slow. 3. You claim that your reasoning algorithm is faster. 4. That means, you've found a heuristic to reason quickly in FOL (assuming your results can be translated back to FOL in polynomial time). More likely though, is that your algorithm is incomplete wrt FOL, ie, there may be some things that FOL can infer but PLN can't. Either that, or your algorithm may be actually slower than FOL. 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
Well, it's still difficult for me to get a handle on how your logic works, I hope you will provide some info in your docs, re the correspondence between FOL and PLN. I think it's fine that you use the term atom in your own way. The important thing is, whatever the objects that you attach probabilities to, that class of objects should correspond to *propositions* in FOL. From there it would be easier for me to understand your ideas. 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
On 6/2/08, Matt Mahoney [EMAIL PROTECTED] wrote: YKY, how are you going to solve the natural language interface problem? You seem to be going down the same path as CYC. What is different about your system? One more point: Yes, my system is similar to Cyc in that it's logic-based. But of course, it will be augmented with probabilities and fuzziness, in some ways yet to be figured out. I guess your idea is that the language model should be the basis of the AGI, whereas my idea is that AGI should be based on logical representation. The difference may not be as great as you think. You may think that natural language is fluid and therefore more suitable for AGI as compared to logic. Let me point out that logic, equipped with learning, can be equally fluid. 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
Ben, I should not say that FOL is the standard of KR, but that it's merely more popular. I think researchers ought to be free to explore whatever they want. Can we simply treat PLN as a black box, so you don't have to explain its internals, and just tell us what are the input and output format? The ideal is to have everyone work on the same KR, but if that's unattainable, the next best thing is to enable different modules to interoperate as easily as possible... 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
More likely though, is that your algorithm is incomplete wrt FOL, ie, there may be some things that FOL can infer but PLN can't. Either that, or your algorithm may be actually slower than FOL. FOL is not an algorithm, it:s a representational formalism... As compared to standard logical theorem-proving algorithms, the design intention is that Novamente/OpenCogs inference algorithms will be vastly more efficient on the average case for those inference problems typically confronting an embodied social organism. Ben --- 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
--- On Mon, 6/2/08, YKY (Yan King Yin) [EMAIL PROTECTED] wrote: YKY, how are you going to solve the natural language interface problem? You seem to be going down the same path as CYC. What is different about your system? One more point: Yes, my system is similar to Cyc in that it's logic-based. But of course, it will be augmented with probabilities and fuzziness, in some ways yet to be figured out. I believe NARS models probabilities and uses induction to adjust them. However, NARS is years from design completion, and then there is the small matter of building the knowledge base (ala Cyc). I guess your idea is that the language model should be the basis of the AGI, whereas my idea is that AGI should be based on logical representation. The difference may not be as great as you think. You may think that natural language is fluid and therefore more suitable for AGI as compared to logic. Let me point out that logic, equipped with learning, can be equally fluid. Do you have any insights on how this learning will be done? Cyc put a lot of effort into a natural language interface and failed. What approach will you use that they have not tried? FOL requires a set of transforms, e.g. All men are mortal - forall X, man(X) - mortal(X) (hard) Socrates is a man - (man(Socrates) (hard) - mortal(Socrates) (easy) - Socrates is mortal (hard). We have known for a long time how to solve the easy parts. The hard parts are AI-complete. You have to solve AI before you can learn the knowledge base. Then after you build it, you won't need it. What is the point? -- Matt Mahoney, [EMAIL PROTECTED] --- 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
YKY, Can you give an example of something expressed in PLN that is very hard or impossible to express in FOL? FYI, I recently run into some issues with my [under-development] formal language (which is being designed for my AGI-user communication) when trying to express statements like: John said that if he knew yesterday what he knows today, he wouldn't do what he did back then. The difficulty might have been specific to my design (because of certain way of semantic meta-data handling), but I thought I would share it just in case. Best, Jiri --- 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
Here are some examples in FOL: Mary is female female(mary) Could be Inheritance Mary female or Evaluation female mary (the latter being equivalent to female(mary) ) but none of these has an uncertain truth value attached... This is a [production] rule: (not to be confused with an inference rule) A female child is called a daughter daughter(X) - child(X) female(X) where universal quantification is assumed. You could say ForAll $X ExtensionalImplication And Evaluation child ($X) Evaluation female ($X) Evaluation daughter($X) which is equivalent to the pred logic formulation you've given. But it will often be more useful to say Implication And Evaluation child ($X) Evaluation female ($X) Evaluation daughter($X which leaves the variable unbound, and which replaces the purely extensional implication with an Implication that is mixed extensional and intensional. And one will normally want to attach an uncertain TV like an indefinite probability to an expression like this, rather than leaving it with a crisp TV. The definition of IntensionalImplication A B is ExtensionalImplication Prop(A) Prop(B) where Prop(X) is the fuzzy set of properties of X The definition of Implication is a weighted average of extensional and intensional implication I guess that gives a flavor of the difference *** bonus question *** Can you give an example of something expressed in PLN that is very hard or impossible to express in FOL? FOL can express anything, as can combinatory logic and a load of other Turing-complete formalisms. However, expressing uncertainty is awkward and inefficient in FOL, as opposed to if one uses a specific mechanism like indefinite truth values. Similarly, expressing intensional relationships is awkward and inefficient in FOL as there is no built in notion of fuzzy sets of properties And there is no notion of assigning a truth value to a formula with unbound variables in FOL, but one can work around this by using variables that are universally bound to a context that is then itself variable (again, more complex and awkward) -- ben --- 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
Ben, Thanks for the answers. One more question about the term atom used in OpenCog. In logic an atom is a predicate applied to some arguments, for example: female(X) female(mary) female(mother(john)) etc. Truth values only apply to propositions, but they may consist of only single atoms as above. But still, there is a distinction. Probabilities should only be attached to propositions, but not to atoms (in logic). Do OpenCog atoms roughly correspond to logical atoms? And what is the counterpart of (logic) propositions in OpenCog? I suggest don't use non-standard terminology 'cause it's very confusing... 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
Re: [agi] OpenCog's logic compared to FOL?
Do OpenCog atoms roughly correspond to logical atoms? Not really And what is the counterpart of (logic) propositions in OpenCog? ExtensionalImplication relations I guess... I suggest don't use non-standard terminology 'cause it's very confusing... So long as it's well-defined, I guess it's OK... The standard terminology leads in wrong conceptual directions alas... ben --- 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=103754539-40ed26 Powered by Listbox: http://www.listbox.com
