Re: Introducing Autobliss 1.0 (was RE: [agi] Nirvana? Manyana? Never!)
--- Jiri Jelinek [EMAIL PROTECTED] wrote: On Nov 11, 2007 5:39 PM, Matt Mahoney [EMAIL PROTECTED] wrote: We just need to control AGIs goal system. You can only control the goal system of the first iteration. ..and you can add rules for it's creations (e.g. stick with the same goals/rules unless authorized otherwise) You can program the first AGI to program the second AGI to be friendly. You can program the first AGI to program the second AGI to program the third AGI to be friendly. But eventually you will get it wrong, and if not you, then somebody else, and evolutionary pressure will take over. But if consciousness does not exist... obviously, it does exist. Belief in consciousness exists. There is no test for the truth of this belief. Consciousness is basically an awareness of certain data and there are tests for that. autobliss passes tests for awareness of its inputs and responds as if it has qualia. How is it fundamentally different from human awareness of pain and pleasure, or is it just a matter of degree? -- Matt Mahoney, [EMAIL PROTECTED] - 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=8660244id_secret=64515425-65dd64
Re: [agi] What best evidence for fast AI?
Bryan Bishop wrote: On Monday 12 November 2007 22:16, Richard Loosemore wrote: If anyone were to throw that quantity of resources at the AGI problem (recruiting all of the planet), heck, I could get it done in about 3 years. ;-) I have done some research on this topic in the last hour and have found that a Connectome Project is in fact in the very early stages out there on the internet: http://iic.harvard.edu/projects/connectome.html http://acenetica.blogspot.com/2005/11/human-connectome.html http://acenetica.blogspot.com/2005/10/mission-to-build-simulated-brain.html http://www.indiana.edu/~cortex/connectome_plos.pdf This is the whole brain emulation approach, I guess (my previous comments were about evolution of brains rather than neural level duplication). But (switching topics to whole brain emulation) there are serious problems with this. It seems quite possible that what we need is a detailed map of every synapse, exact layout of dendritic tree structures, detailed knowledge of the dynamics of these things (they change rapidly) AND wiring between every single neuron. When I say it seems possible I mean that the chance of this information being absolutely necessary in order to understand what the neural system is doing, is so high that we would not want to gamble on them NOT being necessary. So are the researchers working at that level of detail? Egads, no! Here's a quote from the PLOS Computational Biology paper you referenced (above): Attempting to assemble the human connectome at the level of single neurons is unrealistic and will remain infeasible at least in the near future. They are not even going to do it at the resolution needed to see individual neurons?! I think that if they did the whole project at that level of detail it would amount to a possibly interesting hint at some of the wiring, of peripheral interest to people doing work at the cognitive system level. But that is all. I think it would be roughly equivalent to the following: You say to me I want to understand how computers work, in enough detail to build my own and I reply with I can get a you a photo of a motherboard and a 500 by 500 pixel image of the inside of an Intel chip... Richard Loosemore - 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=8660244id_secret=64523531-24742d
Re: Introducing Autobliss 1.0 (was RE: [agi] Nirvana? Manyana? Never!)
Matt Mahoney wrote: --- Jiri Jelinek [EMAIL PROTECTED] wrote: On Nov 11, 2007 5:39 PM, Matt Mahoney [EMAIL PROTECTED] wrote: We just need to control AGIs goal system. You can only control the goal system of the first iteration. ..and you can add rules for it's creations (e.g. stick with the same goals/rules unless authorized otherwise) You can program the first AGI to program the second AGI to be friendly. You can program the first AGI to program the second AGI to program the third AGI to be friendly. But eventually you will get it wrong, and if not you, then somebody else, and evolutionary pressure will take over. This statement has been challenged many times. It is based on assumptions that are, at the very least, extremely questionable, and according to some analyses, extremely unlikely. Richard Loosemore - 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=8660244id_secret=64528236-2fa800
[agi] Human uploading
Richard, I recently saw a talk by Todd Huffman at the Foresight Unconference on the topic of mind uploading technology, and he was specifically showing off techniques for imaging slices of brain, that *do* give the level of biological detail you're thinking of. Topics of discussions were, for example, inferring synaptic strength indirectly from mitochondrial activity. So, the Connectome people may not be taking a sufficiently fine-grained approach to support mind-uploading, but others are trying... Obviously, a detailed map of the brain at the level Todd is thinking of, would be of more than peripheral interest to cognitive scientists. It would not resolve cognitive questions in itself, but would be a wonderful trove of data to use to help validate or refute cognitive theories. -- Ben G On Nov 13, 2007 10:11 AM, Richard Loosemore [EMAIL PROTECTED] wrote: Bryan Bishop wrote: On Monday 12 November 2007 22:16, Richard Loosemore wrote: If anyone were to throw that quantity of resources at the AGI problem (recruiting all of the planet), heck, I could get it done in about 3 years. ;-) I have done some research on this topic in the last hour and have found that a Connectome Project is in fact in the very early stages out there on the internet: http://iic.harvard.edu/projects/connectome.html http://acenetica.blogspot.com/2005/11/human-connectome.html http://acenetica.blogspot.com/2005/10/mission-to-build-simulated-brain.html http://www.indiana.edu/~cortex/connectome_plos.pdfhttp://www.indiana.edu/%7Ecortex/connectome_plos.pdf This is the whole brain emulation approach, I guess (my previous comments were about evolution of brains rather than neural level duplication). But (switching topics to whole brain emulation) there are serious problems with this. It seems quite possible that what we need is a detailed map of every synapse, exact layout of dendritic tree structures, detailed knowledge of the dynamics of these things (they change rapidly) AND wiring between every single neuron. When I say it seems possible I mean that the chance of this information being absolutely necessary in order to understand what the neural system is doing, is so high that we would not want to gamble on them NOT being necessary. So are the researchers working at that level of detail? Egads, no! Here's a quote from the PLOS Computational Biology paper you referenced (above): Attempting to assemble the human connectome at the level of single neurons is unrealistic and will remain infeasible at least in the near future. They are not even going to do it at the resolution needed to see individual neurons?! I think that if they did the whole project at that level of detail it would amount to a possibly interesting hint at some of the wiring, of peripheral interest to people doing work at the cognitive system level. But that is all. I think it would be roughly equivalent to the following: You say to me I want to understand how computers work, in enough detail to build my own and I reply with I can get a you a photo of a motherboard and a 500 by 500 pixel image of the inside of an Intel chip... Richard Loosemore - 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=8660244id_secret=64558273-86797b
Essay - example of how the CSP bites [WAS Re: [agi] What best evidence for fast AI?]
Mark Waser wrote: I'm going to try to put some words into Richard's mouth here since I'm curious to see how close I am . . . . (while radically changing the words). I think that Richard is not arguing about the possibility of Novamente-type solutions as much as he is arguing about the predictability of *very* flexible Novamente-type solutions as they grow larger and more complex (and the difficulty in getting it to not instantaneously crash-and-burn). Indeed, I have heard a very faint shadow of Richard's concerns in your statements about the tuning problems that you had with BioMind. This is true, but not precise enough to capture the true nature of my worry. Let me focus on one aspect of the problem. My goal here is to describe in a little detail how the Complex Systems Problem actually bites in a particular case. Suppose that in some significant part of Novamente there is a representation system that uses probability or likelihood numbers to encode the strength of facts, as in [I like cats](p=0.75). The (p=0.75) is supposed to express the idea that the statement [I like cats] is in some sense 75% true. [Quick qualifier: I know that this oversimplifies the real situation in Novamente, but I need to do this simplification in order to get my point across, and I am pretty sure this will not affect my argument, so bear with me]. We all know that this p value is not quite a probability or likelihood or confidence factor. It plays a very ambigous role in the system, because on the one hand we want it to be very much like a probability in the sense that we want to do calculations with it: we NEED a calculus of such values in order to combine facts in the system to make inferences. But we also do not want to lock ourselves into a particular interpretation of what it means, because we know full well that we do not really have a clear semantics for these numbers. Either way, we have a problem: a fact like [I like cats](p=0.75) is ungrounded because we have to interpret it. Does it mean that I like cats 75% of the time? That I like 75% of all cats? 75% of each cat? Are the cats that I like always the same ones, or is the chance of an individual cat being liked by me something that changes? Does it mean that I like all cats, but only 75% as much as I like my human family, which I like(p=1.0)? And so on and so on. Digging down to the root of this problem (and this is the point where I am skipping from baby stuff to hard core AI) we want these numbers to be semantically compositional and interpretable, but in order to make sure they are grounded, the system itself is going to have to build them interpret them without our help ... and it is not clear that this grounding can be completely implemented. Why is it not clear? Because when you try to build the entire grounding mechanism(s) you are forced to become explicit about what these numbers mean, during the process of building a grounding system that you can trust to be doing its job: you cannot create a mechanism that you *know* is constructing sensible p numbers and facts during all of its development *unless* you finally bite the bullet and say what the p numbers really mean, in fully cashed out terms. [Suppose you did not do this. Suppose you built the grounding mechanism but remained ambiguous about the meaning of the p numbers. What would the resulting system be computing? From end to end it would be building facts with p numbers, but you the human observer would still be imposing an interpretation on the facts. And if you are still doing anything to interpret, it cannot be grounded]. Now, as far as I understand it, the standard approach to this condundrum is that researchers (in Novamente and elsewhere) do indeed make an attempt to disambiguate the p numbers, but they do it by developing more sophisticated logical systems. First, perhaps, error-value bands of p values instead of sharp values. And temporal logic mechanisms to deal with time. Perhaps clusters of p and q and r and s values, each with some slightly different zones of applicability. More generally, people try to give structure to the qualifiers that are appended to the facts: [I like cats](qualfier=value) instead of [I like cats](p=0.75). The question is, does this process of refinement have an end? Does it really lead to a situation where the qualifier is disambiguated and the semantics is clear enough to build a trustworthy grounding system? Is there a closed-form solution to the problem of building a logic that disambiguates the qualifiers? Here is what I think will happen if this process is continued. In order to make the semantics unambiguous enough to let the system ground its own knowledge without the interpretation of p values, researchers will develop more and more sophisticated logics (with more and more structured replacements for that simple p value), until they are
RE: [agi] What best evidence for fast AI?
Response to Mark Waser Mon 11/12/2007 2:42 PM post. MARK Remember that the brain is *massively* parallel. Novamente and any other linear (or minorly-parallel) system is *not* going to work in the same fashion as the brain. Novamente can be parallelized to some degree but *not* to anywhere near the same degree as the brain. I love your speculation and agree with it -- but it doesn't match near-term reality. We aren't going to have brain-equivalent parallelism anytime in the near future. ED I think in five to ten years there could be computers capable of providing every bit as much parallelism as the brain at prices that will allow thousands or hundreds of thousands of them to be sold. But it is not going to happen overnight. Until then the lack of brain level hardware is going to limit AGI. But there are still a lot of high value system that could be built on say $100K to $10M of hardware. You claim we really need experience with computing and controlling activation over large atom tables. I would argue that obtaining such experience should be a top priority for government funders. MARK The node/link architecture is very generic and can be used for virtually anything. There is no rational way to attack it. It is, I believe, going to be the foundation for any system since any system can easily be translated into it. Attacking the node/link architecture is like attacking assembly language or machine code. Now -- are you going to write your AGI in assembly language? If you're still at the level of arguing node/link, we're not communicating well. ED nodes and links are what patterns are made of, and each static pattern can have an identifying node associated with it as well as the nodes and links representing its sub-patterns, elements, the compositions of which it is part, it associations, etc. The system automatically organize patterns into a gen/comp hierarchy. So, I am not just dealing at a node and link level, but they are the basic building blocks. MARK ... I *AM* saying that the necessity of using probabilistic reasoning for day-to-day decision-making is vastly over-rated and has been a horrendous side-road for many/most projects because they are attempting to do it in situations where it is NOT appropriate. The increased, almost ubiquitous adaptation of probabilistic methods is the herd mentality in action (not to mention the fact that it is directly orthogonal to work thirty years older). Most of the time, most projects are using probabilistic methods to calculate a tenth place decimal of a truth value when their data isn't even sufficient for one. If you've got a heavy-duty discovery system, probabilistic methods are ideal. If you're trying to derive probabilities from a small number of English statements (like this raven is white and most ravens are black), you're seriously on the wrong track. If you go on and on about how humans don't understand Bayesian reasoning, you're both correct and clueless in not recognizing that your very statement points out how little Bayesian reasoning has to do with most general intelligence. Note, however, that I *do* believe that probabilistic methods *are* going to be critically important for activation for attention, etc. ED I agree that many approaches accord too much importance to the numerical accuracy and Bayesian purity of their approach, and not enough importance on the justification for the Bayesian formulations they use. I know of one case where I suggested using information that would almost certainly have improved a perception process and the suggestion was refused because it would not fit within the systems probabilistic framework. At an AAAI conference in 1997 I talked to a programmer for a big defense contractor who said he as a fan of fuzzy logic system; that they were so much more simple to get up an running because you didn't have to worry about probabilistic purity. He said his group that used fuzzy logic was getting things out the door that worked faster than the more probability limited competition. So obviously there is something to say for not letting probabilistic purity get in the way of more reasonable approaches. But I still think probabilities are darn important. Even your this raven is white and most ravens are black example involves notions of probability. We attribute probabilities to such statements based on experience with the source of such statements or similar sources of information, and the concept most is a probabilistic one. The reason we humans are so good at reasoning from small data is based on our ability to estimate rough probabilities from similar or generic patterns. MARK The problem with probability-based conflict resolution is that it is a hack to get around insufficient knowledge rather than an attempt to figure out how to get more knowledge ED This agrees with what I said above about not putting enough emphasis on selecting what
[agi] advice-level dev collaboration
I'm looking for a skilled coder from the AGI community who is well familiar with Java/JEE, SWT/JFace, JWS, PHP, Ajax, MySQL, PostgreSQL - under Windows Linux platforms + familiar with Eclipse as well as NetBeans IDE + who has a good sense of application security (e.g. the Acegi stuff and/or other alternatives for handling authentication, authorization, instance-based access control, RBAC, channel security, human user detection capabilities etc). Having also some linguistics related skills would be awesome. I'm NOT offering a paid job and I'm [currently] not planning to ask the developer to write any code for me. I'm relatively skilled developer myself, familiar enough with the above mentioned technology to use it. But using it is one thing and making important architecture decisions is another. I have done lots of coding and some architecture in the M$ world (=significant part of my tech-background). When it comes to the vast open source dev world, I have done coding (I'm a Java/Oracle pro now) but not much of the architecture yet (even though I'm not really clueless). So the help I'm looking for would be mostly architecture-advice level, occasionally slipping into specific coding details. Nothing terribly time-demanding (I'm also busy with lots of other stuff). Just occasional email exchange about highly technical topics. Results of the online research are sometimes too ambiguous and not that easy to evaluate. If you are the all-knowing guru I'm looking for and willing to help, please get in touch through my private gmail account. Thanks, Jiri Jelinek - 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=8660244id_secret=64603883-e8db13
Re: Essay - example of how the CSP bites [WAS Re: [agi] What best evidence for fast AI?]
Richard, The idea of the PLN semantics underlying Novamente's probabilistic truth values is that we can have **both** -- simple probabilistic truth values without highly specific interpretation -- more complex, logically refined truth values, when this level of precision is necessary To make the discussion more concrete, I'll use a specfic example to do with virtual animals in Second Life. Our first version of the virtual pets won't use PLN in this sort of way, it'll be focused on MOSES evolutionary learning; but, this is planned for the second version and is within the scope of what Novamente can feasibly be expected to do with modest effort. Consider an avatar identified as Bob_Yifu And, consider the concept of friend, which is a ConceptNode -- associated to the WordNode friend via a learned ReferenceLink -- defined operationally via a number of links such as ImplicationLink AND InheritanceLink X friend EvaluationLink near (I, X) Pleasure (this one just says that being near a friend confers pleasure. Other links about friendship may contain knowledge such as that friends often give one food, friends help one find things, etc.) The concept of friend may be learned, via mining of the animal's experience-base -- basically, this is a matter of learning that there are certain predicates whose SatisfyingSets (the set of Atoms that fulfill the predicate) have significant intersection, and creating a ConceptNode to denote that intersection. Then, once the concept of friend has been formed, more links pertaining to it may be learned via mining the experience base and via inference rules. Then, we can may find that InheritanceLink Bob_Yifu friend .9,1 (where the .9,1 is an interval probability, interpreted according to the indefinite probabilities framework) and this link mixes intensional and extensional inheritance, and thus is only useful for heuristic reasoning (which however is a very important kind). What this link means is basically that Bob_Yifu's node in the memory has a lot of the same links as the friend node -- or rather, that it **would**, if all its links were allowed to exist rather than being pruned to save memory. So, note that the semantics are actually tied to the mind itself. Or we can make more specialized logical constructs if we really want to, denoting stuff like -- at certain times Bob_Yifu is a friend -- Bob displays some characteristics of friendship very strongly, and others not at all -- etc. We can also do crude, heuristic contextualization like ContextLink .7,.8 home InheritanceLink Bob_Yifu friend which suggests that Bob is less friendly at home than in general. Again this doesn't capture all the subtleties of Bob's friendship in relation to being at home -- and one could do so if one wanted to, but it would require introducing a larger complex of nodes and links, which is not always the most appropriate thing to do. The PLN inference rules are designed to give heuristically correct conclusions based on heuristically interpreted links; or more precise conclusions based on more precisely interpreted links. Finally, the semantics of PLN relationships is explicitly an **experiential** semantics. (One of the early chapters in the PLN book, to appear via Springer next year, is titled Experiential Semantics.) So, all node and link truth values in PLN are intended to be settable and adjustable via experience, rather than via programming or importation from databases or something like that. Now, the above example is of course a quite simple one. Discussing a more complex example would go beyond the scope of what I'm willing to do in an email conversation, but the mechanisms I've described are not limited to such simple examples. I am aware that identifying Bob_Yifu as a coherent, distinct entity is a problem faced by humans and robots, and eliminated via the simplicity of the SL environment. However, there is detailed discussion in the (proprietary) NM book of how these same mechanisms may be used to do object recognition and classification, as well. You may of course argue that these mechanisms won't scale up to large knowledge bases and rich experience streams. I believe that they will, and have arguments but not rigorous proofs that they will. -- Ben G On Nov 13, 2007 12:34 PM, Richard Loosemore [EMAIL PROTECTED] wrote: Mark Waser wrote: I'm going to try to put some words into Richard's mouth here since I'm curious to see how close I am . . . . (while radically changing the words). I think that Richard is not arguing about the possibility of Novamente-type solutions as much as he is arguing about the predictability of *very* flexible Novamente-type solutions as they grow larger and more complex (and the difficulty in getting it to not instantaneously crash-and-burn). Indeed, I have heard a very faint shadow of Richard's concerns in your statements about the tuning problems that you had
Re: [agi] What best evidence for fast AI?
For example, what is the equivalent of the activation control (or search) algorithm in Google sets. They operate over huge data. I bet the algorithm for calculating their search or activation is relatively simple (much, much, much less than a PhD theses) and look what they can do. So I think one path is to come up with applications that can use and reason with large data, having roughly world knowledge-like sparseness, (such as NL data) and start with relatively simple activation algorithms and develop then from the ground up. Google, I believe, does reasoning about word and phrase co-occurrence using a combination of Bayes net learning with EM clustering (this is based on personal conversations with folks who have worked on related software there). The use of EM helps the Bayes net approach scale. Bayes nets are good for domains like word co-occurence probabilities, in which the relevant data is relatively static. They are not much good for real-time learning. Unlike Bayes nets, the approach taken in PLN and NARS allows efficient uncertain reasoning in dynamic environments based on large knowledge bases (at least in principle, based on the math, algorithms and structures; we haven't proved it yet). -- Ben G - 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=8660244id_secret=64609544-b69ea5
Re: [agi] Human uploading
Yes, I thought I had heard of people trying more ambitious techniques, but in the cases I heard of (can't remember where now) the tradeoffs always left the approach hanging on one of the issues: for example, was he talking about scanning microchondrial activity in vivo, in real time, across the whole brain?!! The mind boggles. [Uh, and it probably would, if you were the subject]. Some people think they can do very thin slices, but they are in defuncto, not in vivo. Yes, Todd believes (like most mind uploading experts) that the most practical approach to mind uploading in the near term is to slice a dead brain and scan it in. Doing uploading on live brains is bound to be far more technologically demanding, so it makes sense to focus on uploading fresh-killed brains first. Couldn't see any good references to this. It was a talk, not a publication. Not sure if it was videotaped or not. -- Ben - 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=8660244id_secret=64610913-6e5f3d
Re: Essay - example of how the CSP bites [WAS Re: [agi] What best evidence for fast AI?]
Mike Tintner wrote: RL:Suppose that in some significant part of Novamente there is a representation system that uses probability or likelihood numbers to encode the strength of facts, as in [I like cats](p=0.75). The (p=0.75) is supposed to express the idea that the statement [I like cats] is in some sense 75% true. This essay seems to be a v.g. demonstration of why the human system almost certainly does not use numbers or anything like, as stores of value - but raw, crude emotions. How much do you like cats [or marshmallow ice cream]? Miaow//[or yummy] [those being an expression of internal nervous and muscular impulses] And black cats [or strawberry marshmallow] ? Miaow-miaoww![or yummy yummy] . It's crude but it's practical. It is all a question of what role the numbers play. Conventional AI wants them at the surface, and transparently interpretable. I am not saying that there are no numbers, but only that they are below the surface, and not directly interpretable. that might or might not gibe with what you are saying ... although I would not go so far as to put it in the way you do. Richard Loosemore - 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=8660244id_secret=64636829-14d428
Re: Essay - example of how the CSP bites [WAS Re: [agi] What best evidence for fast AI?]
Ben, Unfortunately what you say below is tangential to my point, which is what happens when you reach the stage where you cannot allow any more vagueness or subjective interpretation of the qualifiers, because you have to force the system to do its own grounding, and hence its own interpretation. What you gave below was a sketch of some more elaborate 'qualifier' mechanisms. But I described the process of generating more and more elaborate qualifier mechanisms in the body of the essay, and said why this process was of no help in resolving the issue. Richard Loosemore Benjamin Goertzel wrote: Richard, The idea of the PLN semantics underlying Novamente's probabilistic truth values is that we can have **both** -- simple probabilistic truth values without highly specific interpretation -- more complex, logically refined truth values, when this level of precision is necessary To make the discussion more concrete, I'll use a specfic example to do with virtual animals in Second Life. Our first version of the virtual pets won't use PLN in this sort of way, it'll be focused on MOSES evolutionary learning; but, this is planned for the second version and is within the scope of what Novamente can feasibly be expected to do with modest effort. Consider an avatar identified as Bob_Yifu And, consider the concept of friend, which is a ConceptNode -- associated to the WordNode friend via a learned ReferenceLink -- defined operationally via a number of links such as ImplicationLink AND InheritanceLink X friend EvaluationLink near (I, X) Pleasure (this one just says that being near a friend confers pleasure. Other links about friendship may contain knowledge such as that friends often give one food, friends help one find things, etc.) The concept of friend may be learned, via mining of the animal's experience-base -- basically, this is a matter of learning that there are certain predicates whose SatisfyingSets (the set of Atoms that fulfill the predicate) have significant intersection, and creating a ConceptNode to denote that intersection. Then, once the concept of friend has been formed, more links pertaining to it may be learned via mining the experience base and via inference rules. Then, we can may find that InheritanceLink Bob_Yifu friend .9,1 (where the .9,1 is an interval probability, interpreted according to the indefinite probabilities framework) and this link mixes intensional and extensional inheritance, and thus is only useful for heuristic reasoning (which however is a very important kind). What this link means is basically that Bob_Yifu's node in the memory has a lot of the same links as the friend node -- or rather, that it **would**, if all its links were allowed to exist rather than being pruned to save memory. So, note that the semantics are actually tied to the mind itself. Or we can make more specialized logical constructs if we really want to, denoting stuff like -- at certain times Bob_Yifu is a friend -- Bob displays some characteristics of friendship very strongly, and others not at all -- etc. We can also do crude, heuristic contextualization like ContextLink .7,.8 home InheritanceLink Bob_Yifu friend which suggests that Bob is less friendly at home than in general. Again this doesn't capture all the subtleties of Bob's friendship in relation to being at home -- and one could do so if one wanted to, but it would require introducing a larger complex of nodes and links, which is not always the most appropriate thing to do. The PLN inference rules are designed to give heuristically correct conclusions based on heuristically interpreted links; or more precise conclusions based on more precisely interpreted links. Finally, the semantics of PLN relationships is explicitly an **experiential** semantics. (One of the early chapters in the PLN book, to appear via Springer next year, is titled Experiential Semantics.) So, all node and link truth values in PLN are intended to be settable and adjustable via experience, rather than via programming or importation from databases or something like that. Now, the above example is of course a quite simple one. Discussing a more complex example would go beyond the scope of what I'm willing to do in an email conversation, but the mechanisms I've described are not limited to such simple examples. I am aware that identifying Bob_Yifu as a coherent, distinct entity is a problem faced by humans and robots, and eliminated via the simplicity of the SL environment. However, there is detailed discussion in the (proprietary) NM book of how these same mechanisms may be used to do object recognition and classification, as well. You may of course argue that these mechanisms won't scale up to large knowledge bases and rich experience streams. I believe that they will, and have arguments but not rigorous proofs that they will. -- Ben G On Nov 13, 2007 12:34 PM, Richard Loosemore
Re: Essay - example of how the CSP bites [WAS Re: [agi] What best evidence for fast AI?]
On Nov 13, 2007 2:37 PM, Richard Loosemore [EMAIL PROTECTED] wrote: Ben, Unfortunately what you say below is tangential to my point, which is what happens when you reach the stage where you cannot allow any more vagueness or subjective interpretation of the qualifiers, because you have to force the system to do its own grounding, and hence its own interpretation. I don't see why you talk about forcing the system to do its own grounding -- the probabilities in the system are grounded in the first place, as they are calculated based on experience. The system observes, records what it sees, abstracts from it, and chooses actions that it guess will fulfill its goals. Its goals are ultimately grounded in in-built feeling-evaluation routines, measuring stuff like amount of novelty observed, amount of food in system etc. So, the system sees and then acts ... and the concepts it forms and uses are created/used based on their utility in deriving appropriate actions. There is no symbol-grounding problem except in the minds of people who are trying to interpret what the system does, and get confused. Any symbol used within the system, and any probability calculated by the system, are directly grounded in the system's experience. There is nothing vague about an observation like Bob_Yifu was observed at time-stamp 599933322, or a fact Command 'wiggle ear' was sent at time-stamp 54. These perceptions and actions are the root of the probabilities the system calculated, and need no further grounding. What you gave below was a sketch of some more elaborate 'qualifier' mechanisms. But I described the process of generating more and more elaborate qualifier mechanisms in the body of the essay, and said why this process was of no help in resolving the issue. So, if a system can achieve its goals based on choosing procedures that it thinks are likely to achieve its goals, based on the knowledge it gathered via its perceived experience -- why do you think it has a problem? I don't really understand your point, I guess. I thought I did -- I thought your point was that precisely specifying the nature of a conditional probability is a rats-nest of complexity. And my response was basically that in Novamente we don't need to do that, because we define conditional probabilities based on the system's own knowledge-base, i.e. Inheritance A B .8 means If A and B were reasoned about a lot, then A would (as measred by an weighted average) have 80% of the relationships that B does But apparently you were making some other point, which I did not grok, sorry... Anyway, though, Novamente does NOT require logical relations of escalating precision and complexity to carry out reasoning, which is one thing you seemed to be assuming in your post. Ben - 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=8660244id_secret=64644318-8bbdee
Re: [agi] What best evidence for fast AI?
On Mon, Nov 12, 2007 at 08:44:58PM -0500, Mark Waser wrote: So perhaps the AGI question is, what is the difference between a know-it-all mechano-librarian, and a sentient being? I wasn't assuming a mechano-librarian. I was assuming a human that could (and might be trained to) do some initial translation of the question and some final rephrasing of the answer. I'm surprised by your answer. I don't see that the hardest part of agi is NLP i/o. To put it into perspective: one can fake up some trivial NLP i/o now, and with a bit of effort, one can improve significantly on that. Sure, it would be child-like conversation, and the system would be incapable of learning new idioms, expressions, etc., but I don't see that you'd need a human to translate the question into some formal reasoning-engine language. The hard part of NLP is being able to read complex texts, whether Alexander Pope or Karl Marx; but a basic NLP i/o interface stapled to a reasoning engine doesn't need to really do that, or at least not well. Yet, these two stapled toegether would qualify as a mechano-librarian for me. To me, the hard part is still the reasoning engine itself, and the pruning, and the tailoring of responses to the topic at hand. So let me rephrase the question: If one had 1) A reasoing engine that could provide short yet appropriate responses to questions, 2) A simple NLP interface to the reasoning engine would that be AGI? I imagine most folks would say no, so let me throw in: 3) System can learn new NLP idioms, so that it can eventually come to understand those sentences and paragraphs that make Karl Marx so hard to read. With this enhanced reading ability, it could then presumably become a know-it-all ultra-question-answerer. Would that be AGI? Or is there yet more? Well, of course there's more: one expects creativity, aesthetics, ethics. But we know just about nothing about that. This is the thing that I think is relevent to Robin Hanson's original question. I think we can build 1+2 is short order, and maybe 3 in a while longer. But the result of 1+2+3 will almost surely be an idiot-savant: knows everything about horses, and can talk about them at length, but, like a pedantic lecturer, the droning will put you asleep. So is there more to AGI, and exactly how do way start laying hands on that? --linas - 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=8660244id_secret=64661358-af169f
Re: [agi] What best evidence for fast AI?
This is the thing that I think is relevent to Robin Hanson's original question. I think we can build 1+2 is short order, and maybe 3 in a while longer. But the result of 1+2+3 will almost surely be an idiot-savant: knows everything about horses, and can talk about them at length, but, like a pedantic lecturer, the droning will put you asleep. So is there more to AGI, and exactly how do way start laying hands on that? --linas I think that evolutionary-learning-type methods play a big role in creativity. I elaborated on this quite a bit toward the end of my 1997 book From Complexity to Creativity. Put simply, inference is ultimately a local search method -- inference rules, even heuristic and speculative ones, always lead you step by step from what you know into the unknown. This makes you, as you say, like a pedantic lecturer. OTOH, evolutionary algorithms can take big creative leaps. This is one reason why the MOSES evolutionary algorithm plays a big role in the Novamente design (the other, related reason being that evolutionary learning is better than logical inference for many kinds of procedure learning). Integrating evolution with logic is key to intelligence. The brain does it, I believe, via -- implementing logic via Hebbian learning (neuron-level Hebb stuff leading to PLN-like logic stuff on the neural-assembly level) -- implementing evolution via Edelman-style Neural Darwinist neural map evolution (which ultimately bottoms out in Hebbian learning too) Novamente seeks to enable this integration via grounding both inference and evolutionary learning in probability theory. -- Ben G -- Ben G - 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=8660244id_secret=64667888-a48aa3
Re: Introducing Autobliss 1.0 (was RE: [agi] Nirvana? Manyana? Never!)
--- Richard Loosemore [EMAIL PROTECTED] wrote: Matt Mahoney wrote: --- Jiri Jelinek [EMAIL PROTECTED] wrote: On Nov 11, 2007 5:39 PM, Matt Mahoney [EMAIL PROTECTED] wrote: We just need to control AGIs goal system. You can only control the goal system of the first iteration. ..and you can add rules for it's creations (e.g. stick with the same goals/rules unless authorized otherwise) You can program the first AGI to program the second AGI to be friendly. You can program the first AGI to program the second AGI to program the third AGI to be friendly. But eventually you will get it wrong, and if not you, then somebody else, and evolutionary pressure will take over. This statement has been challenged many times. It is based on assumptions that are, at the very least, extremely questionable, and according to some analyses, extremely unlikely. I guess it will continue to be challenged until we can do an experiment to prove who is right. Perhaps you should challenge SIAI, since they seem to think that friendliness is still a hard problem. -- Matt Mahoney, [EMAIL PROTECTED] - 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=8660244id_secret=64668559-1aacd3
Re: Essay - example of how the CSP bites [WAS Re: [agi] What best evidence for fast AI?]
On Tue, Nov 13, 2007 at 12:34:51PM -0500, Richard Loosemore wrote: Suppose that in some significant part of Novamente there is a representation system that uses probability or likelihood numbers to encode the strength of facts, as in [I like cats](p=0.75). The (p=0.75) is supposed to express the idea that the statement [I like cats] is in some sense 75% true. Either way, we have a problem: a fact like [I like cats](p=0.75) is ungrounded because we have to interpret it. Does it mean that I like cats 75% of the time? That I like 75% of all cats? 75% of each cat? Are the cats that I like always the same ones, or is the chance of an individual cat being liked by me something that changes? Does it mean that I like all cats, but only 75% as much as I like my human family, which I like(p=1.0)? And so on and so on. Eh? You are standing at the proverbial office water coooler, and Aneesh says Wen likes cats. On your drive home, you mind races .. does this mean that Wen is a cat fancier? You were planning on taking Wen out on a date, and this tidbit of information could be useful ... when you try to build the entire grounding mechanism(s) you are forced to become explicit about what these numbers mean, during the process of building a grounding system that you can trust to be doing its job: you cannot create a mechanism that you *know* is constructing sensible p numbers and facts during all of its development *unless* you finally bite the bullet and say what the p numbers really mean, in fully cashed out terms. But has a human, asking Wen out on a date, I don't really know what Wen likes cats ever really meant. It neither prevents me from talking to Wen, or from telling my best buddy that ...well, I know, for instance, that she likes cats... Lack of grounding is what makes humour funny, you can do a whole Pygmalion / Seinfeld episode on she likes cats. --linas - 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=8660244id_secret=64672202-2af80e
Re: Essay - example of how the CSP bites [WAS Re: [agi] What best evidence for fast AI?]
But has a human, asking Wen out on a date, I don't really know what
Wen likes cats ever really meant. It neither prevents me from talking
to Wen, or from telling my best buddy that ...well, I know, for
instance, that she likes cats...
yes, exactly...
The NLP statement Wen likes cats is vague in the same way as the
Novamente or NARS relationship
EvaluationLink
likes
ListLink
Wen
cats
is vague The vagueness passes straight from NLP into the internal KR,
which is how it should be.
And that same vagueness may be there if the relationship is learned via
inference based on experience, rather than acquired by natural language.
I.e., if the above relationship is inferred, it may just mean that
{the relationship between Wen and cats} shares many relationships with
other person/object relationships that have been categorized as 'liking'
before
In this case, the system can figure out that Wen likes cats without ever
actually making explicit what this means. All it knows is that, whatever it
means,
it's the same thing that was meant in other circumstances where liking
was used as a label.
So, vagueness can not only be important into an AI system from natural
language,
but also propagated around the AI system via inference.
This is NOT one of the trickier things about building probabilistic AGI,
it's really
kind of elementary...
-- Ben G
-
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Re: Essay - example of how the CSP bites [WAS Re: [agi] What best evidence for fast AI?]
So, vagueness can not only be important imported, I meant into an AI system from natural language, but also propagated around the AI system via inference. This is NOT one of the trickier things about building probabilistic AGI, it's really kind of elementary... -- Ben G - 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=8660244id_secret=64674943-4b25e0
Re: [agi] Human uploading
It seems quite possible that what we need is a detailed map of every synapse, exact layout of dendritic tree structures, detailed knowledge of the dynamics of these things (they change rapidly) AND wiring between every single neuron. An example of automatic detection of neurons and their processes from BrainMaps data. This is from layer 6 of the cortex of a monkey. Green indicates the detected cell bodies. http://farm1.static.flickr.com/137/360938913_6b7ffb9cbe_o.jpg I think the first structural upload of an entire brain may not be far away. There are significant computational resources required (there's a lot of data and multiple slices need to be carefully registered since they distort non-uniformly) but I think the necessary compute power and storage will be available cheaply before this decade is out. Reverse engineering the detailed structure of the brain won't give us a mind upload, but it will be a useful first step in that direction, greatly assisting with the development of plausible theories about how the brain really operates. - 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=8660244id_secret=64677853-23d7fb
Re: [agi] Human uploading
Bob, The two biologists I know who are deep into mind uploading (Randal Koene and Todd Huffman) both agree with your basic assessment, I believe... ben g On Nov 13, 2007 4:37 PM, Bob Mottram [EMAIL PROTECTED] wrote: It seems quite possible that what we need is a detailed map of every synapse, exact layout of dendritic tree structures, detailed knowledge of the dynamics of these things (they change rapidly) AND wiring between every single neuron. An example of automatic detection of neurons and their processes from BrainMaps data. This is from layer 6 of the cortex of a monkey. Green indicates the detected cell bodies. http://farm1.static.flickr.com/137/360938913_6b7ffb9cbe_o.jpg I think the first structural upload of an entire brain may not be far away. There are significant computational resources required (there's a lot of data and multiple slices need to be carefully registered since they distort non-uniformly) but I think the necessary compute power and storage will be available cheaply before this decade is out. Reverse engineering the detailed structure of the brain won't give us a mind upload, but it will be a useful first step in that direction, greatly assisting with the development of plausible theories about how the brain really operates. - 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=8660244id_secret=64678817-6521b4
Re: [agi] What best evidence for fast AI?
I don't see that the hardest part of agi is NLP i/o. I didn't say that i/o was the hardest part of agi. Truly understanding NLP is agi-complete though. And please, get off this kick of just faking something up and thinking that because you can create a shallow toy example that holds for ten seconds that you've answered *anything*. That's the *narrow ai* approach. - Original Message - From: Linas Vepstas [EMAIL PROTECTED] To: agi@v2.listbox.com Sent: Tuesday, November 13, 2007 4:01 PM Subject: Re: [agi] What best evidence for fast AI? On Mon, Nov 12, 2007 at 08:44:58PM -0500, Mark Waser wrote: So perhaps the AGI question is, what is the difference between a know-it-all mechano-librarian, and a sentient being? I wasn't assuming a mechano-librarian. I was assuming a human that could (and might be trained to) do some initial translation of the question and some final rephrasing of the answer. I'm surprised by your answer. I don't see that the hardest part of agi is NLP i/o. To put it into perspective: one can fake up some trivial NLP i/o now, and with a bit of effort, one can improve significantly on that. Sure, it would be child-like conversation, and the system would be incapable of learning new idioms, expressions, etc., but I don't see that you'd need a human to translate the question into some formal reasoning-engine language. The hard part of NLP is being able to read complex texts, whether Alexander Pope or Karl Marx; but a basic NLP i/o interface stapled to a reasoning engine doesn't need to really do that, or at least not well. Yet, these two stapled toegether would qualify as a mechano-librarian for me. To me, the hard part is still the reasoning engine itself, and the pruning, and the tailoring of responses to the topic at hand. So let me rephrase the question: If one had 1) A reasoing engine that could provide short yet appropriate responses to questions, 2) A simple NLP interface to the reasoning engine would that be AGI? I imagine most folks would say no, so let me throw in: 3) System can learn new NLP idioms, so that it can eventually come to understand those sentences and paragraphs that make Karl Marx so hard to read. With this enhanced reading ability, it could then presumably become a know-it-all ultra-question-answerer. Would that be AGI? Or is there yet more? Well, of course there's more: one expects creativity, aesthetics, ethics. But we know just about nothing about that. This is the thing that I think is relevent to Robin Hanson's original question. I think we can build 1+2 is short order, and maybe 3 in a while longer. But the result of 1+2+3 will almost surely be an idiot-savant: knows everything about horses, and can talk about them at length, but, like a pedantic lecturer, the droning will put you asleep. So is there more to AGI, and exactly how do way start laying hands on that? --linas - 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=8660244id_secret=64683060-82d4be
Re: [agi] advice-level dev collaboration
Hi Jiri, The [agi] list is billed as being for more technical discussions about current AGI projects. I joined this particular list hoping to find all levels of discussions of technical details of AGI construction and theory. I would therefore hope that many of your questions would/should be on-topic for this mailing list. Why not try this list, and then move to the private discussion model (or start an [agi-developer] list) if there's a backlash? -Benjamin Johnston Jiri Jelinek wrote: I'm looking for a skilled coder from the AGI community who is well familiar with Java/JEE, SWT/JFace, JWS, PHP, Ajax, MySQL, PostgreSQL - under Windows Linux platforms + familiar with Eclipse as well as NetBeans IDE + who has a good sense of application security (e.g. the Acegi stuff and/or other alternatives for handling authentication, authorization, instance-based access control, RBAC, channel security, human user detection capabilities etc). Having also some linguistics related skills would be awesome. I'm NOT offering a paid job and I'm [currently] not planning to ask the developer to write any code for me. I'm relatively skilled developer myself, familiar enough with the above mentioned technology to use it. But using it is one thing and making important architecture decisions is another. I have done lots of coding and some architecture in the M$ world (=significant part of my tech-background). When it comes to the vast open source dev world, I have done coding (I'm a Java/Oracle pro now) but not much of the architecture yet (even though I'm not really clueless). So the help I'm looking for would be mostly architecture-advice level, occasionally slipping into specific coding details. Nothing terribly time-demanding (I'm also busy with lots of other stuff). Just occasional email exchange about highly technical topics. Results of the online research are sometimes too ambiguous and not that easy to evaluate. If you are the all-knowing guru I'm looking for and willing to help, please get in touch through my private gmail account. Thanks, Jiri Jelinek - 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=8660244id_secret=64729218-e0cbdf
Re: [agi] Human uploading
Ben, This is all very interesting work. I have heard of brain slicing before, as well as viral gene therapy to add a way for our neurons to debug themselves into the blood stream, which is not yet technologically possible (or here yet, rather), and the age-old concept of using MNT to signal data about our neurons, synapses, etc. There is also the concept of incrementally replacing the brain, component by component, also requiring MNT, or possibly taking out regions of the brain and replacing them with equivalents and re-training those portions somehow, obviously less effective with memories. I have been thinking that if we do not care for *pure* mind uploading, we should also be focusing on how long we can keep regions of the brain alive on life support with MEAs or DNIs (a type of BCI) to connect it back to the rest of the brain or a digitized brain. If we can do this well enough, we can keep our minds alive long enough to see the day when we have more options for mind uploading. - Bryan - 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=8660244id_secret=64756752-3c621b
Re: [agi] What best evidence for fast AI?
On Tuesday 13 November 2007 09:11, Richard Loosemore wrote: This is the whole brain emulation approach, I guess (my previous comments were about evolution of brains rather than neural level duplication). Ah, you are right. But this too is an interesting topic. I think that the order of magnitudes for whole brain emulation, connectome, and similar evolutionary methods, are roughly the same, but I haven't done any calculations. It seems quite possible that what we need is a detailed map of every synapse, exact layout of dendritic tree structures, detailed knowledge of the dynamics of these things (they change rapidly) AND wiring between every single neuron. Hm. It would seem that we could have some groups focusing on neurons, another on types of neurons, another on dendritic tree structures, some more on the abstractions of dendritic trees, etc. in an up-*and*-down propagation hierarchy so that the abstract processes of the brain are studied just as well as the in-betweens of brain architecture. I think that if they did the whole project at that level of detail it would amount to a possibly interesting hint at some of the wiring, of peripheral interest to people doing work at the cognitive system level. But that is all. You see no more possible value of such a project? - Bryan - 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=8660244id_secret=64757679-f3c1ec
Re: [agi] advice-level dev collaboration
On Tuesday 13 November 2007 17:12, Benjamin Johnston wrote: Why not try this list, and then move to the private discussion model (or start an [agi-developer] list) if there's a backlash? I'd certainly join. - Bryan - 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=8660244id_secret=64758692-14dcfa
[agi] Relativistic irrationalism
Would be great if people could poke the following with their metaphorical sticks: Imagine two agents A(i) each one with a utility function F(i), capability level C(i) and no knowledge as to the other agents F and C values. Both agents are given equal resources and are tasked with devising the most efficient and effective way to maximize their respective utility with said resources. Scenario 1: Both agents have fairly similar utility functions F(1) = F(2), level of knowledge, cognitive complexity, experience - in short capability C(1) = C(2) - and a high level of mutual trust T(1-2) = T(2-1) = 1. They will quickly agree on the way forward, pool their resources and execute their joint plan. Rather boring. Scenario 2: Again we assume F(1) = F(2), however C(1) C(2) - again T(1-2) = T(2-1) = 1. The more capable agent will devise a plan, the less capable agent will provide its resources and execute the plan trusted by C(2). A bit more interesting. Scenario 3: F(1) = F(2), C(1) C(2) but this time T(1-2) = 1 and T(2-1) = 0.5 meaning the less powerful agent assumes with a probability of 50% that A(1) is in fact a self serving optimizer who's difference in plan will turn out to be decremental to A(2) while A(1) is certain that this is all just one big misunderstanding. The optimal plan devised under scenario 2 will now face opposition by A(2) although it would be in A(2)'s best interest to actually support it with its resources to maximize (F2) while A(1) will see A(2)'s objection as being detrimental to maximizing their shared utility function. Fairly interesting: based on lack of trust and differences in capability each agent perceives the other agent's plan as being irrational from their respective points of view. Under scenario 3, both agents now have a variety of strategies at their disposal: 1. deny pooling of part or all of ones resources = If we do not do it my way you can do it alone. 2. use resources to sabotage the other agent's plan = I must stop him with these crazy ideas! 3. deceive the other agent in order to skew how the other agent is deploying strategies 1 and 2 4. spend resources to explain the plan to the other agent = Ok - let's help him see the light 5. spend resources on self improvement to understand the other agent's plan better = Let's have a closer look, the plan might not be so bad after all 6. strike a compromise to ensure a higher level of pooled resources = If we don't compromise we both loose out Number 1 is a given under scenario 3. Number 2 is risky, particularly as it would cause a further reduction in trust on both sides if this strategy gets deployed assuming the other party would find out similarly with number 3. Number 4 seems like the way to go but may not always work particularly with large differences in C(i) among the agents. Number 5 is a likely strategy with a fairly high level of trust. Most likely however is strategy 6. Striking a compromise is trust building in repeated encounters and thus promises less objection and thus higher total payoff the next times around. Assuming the existence of an arguably optimal path leading to a maximally possible satisfaction of a given utility function anything else would be irrational. Actually such a maximally intelligent algorithm exists in the form of Hutter http://www.hutter1.net/ai/ai.htm's universal algorithmic agent AIXI http://citeseer.ist.psu.edu/555887.html. The only problem being however that the execution of said algorithm requires infinite resources and is thus rather unpractical as every decision will always have to be made under resource constrains. Consequentially every decision will be irrational to that degree that it differs from the unknowable optimal path that AIXI would produce. Throw in a lack of trust and varying levels of capability among the agents and all agents will always have to adopt their plans and strike a compromise based on the other agent's relativistic irrationality independent of their capabilities in oder to minimize the other agents objection cost and thus maximizing their respective utility function. -- Stefan Pernar 3-E-101 Silver Maple Garden #6 Cai Hong Road, Da Shan Zi Chao Yang District 100015 Beijing P.R. CHINA Mobil: +86 1391 009 1931 Skype: Stefan.Pernar - 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=8660244id_secret=64805839-967aa4
