That Encapsulation concept method would work in certain situations. I agree that summaries are important, I have always felt that at least half an AI data base would have to be indexing which is the nearly the same as what you are talking about. The problem is that a subsystem can be related to a great number of other subsystems and if we agree that the componential model is a fundamental requirement for AGI then this can lead to an immense potential for complexity. I believe that we need better ways to efficiently represent and use massive numbers of possible interrelations. I think it is a mathematical problem that hasn't been solved yet.
There are times we need to expand the search space and one of those times is when we need to think outside the box. We need to be able to reevaluate a situation quickly and this too may be a form of thinking outside the box. I have also been interested in boundaries for a long time. In order to understand mathematics I think it is necessary to have some grounded concept of boundaries, like those from games. But on the other hand it is also necessary to be able to think outside the box and realize that a boundary does not have to be fixed or obvious or simple or visible and so on. So in order to understand mathematics or visual-based programming or game programming you have to be able to both draw on a childish experience with games and boundaries, but then you also have to be able to draw on a completely different basis for boundaries as well even up to the point that they are seen as part of a model of transcendence. For example, the boundary can be the game sometimes. I think that there must be a way to efficiently represent and use heavily interrelated data objects. Contemporary methods of statistics, logic, numerical methods and neural networks haven't worked. I think something is missing from contemporary software methods, but I might be wrong. I didn't think that they would get Watson to work a year or two ago. Jim Bromer On Thu, Dec 6, 2012 at 11:06 AM, Aaron Hosford <[email protected]> wrote: > I've pointed to the Encapsulation concept from Object Oriented paradigm > before as a means for limiting complexity. Packing up highly interrelated > subsystems recursively and extracting out summaries of important/relevant > expected behavior for each system expressed independently of the details of > the underlying processes that lead to that behavior, allows for the search > space to be fragmented automatically based on degree of connectivity. The > expected behavior of the system can be summarized by running a > simulation-based search among the interactions of only its subsystems, > given the events that might affect the system. It is a means for > automatically generating effective heuristics which functions independently > of the knowledge domain. A summary is less complex, by definition, and so > working with summaries reduces the combinatorial explosion to something > more manageable at the price of sometimes missing seemingly insignificant > details. > > This also explains why we think in terms of objects in the first place. > There is no reason to think that just because we draw a line around a set > of phenomena and call them an object or a kind of object, that the instance > or type we have isolated is necessarily an inherently distinct thing in > reality. The bounds we draw around those phenomena (encapsulating them) are > in our minds, not in reality, but they are difficult to recognize as such > because they're the very things we use for recognition. Why would we add > this new information to a system which is already overwhelmed by > information? To organize that information flood into something manageable. > > > > On Thu, Dec 6, 2012 at 6:08 AM, Jim Bromer <[email protected]> wrote: > >> Piaget Modeler <[email protected]> wrote: >> Integration is combining different concepts via their attributes akin to >> crossover or memetic recombination. >> ... >> I think complexity can be an issue if you have one huge problem and one >> huge model. >> Search spaces become ridiculous very fast. However, if you engineer a >> system so >> that you are not dealing with one large problem but lots of small >> problems, I think its >> feasible to engineer solutions to manage the complexity of each problem >> reasonably. >> ------------------------------- >> >> The wikipedia definition of memetics was interesting. Assuming that I >> can make a pretty good guess about how your idea of memetic recombination >> might work, I would say that your imagined usage of the method has some >> serious problems. First, a meme cannot be modelled in the same way a >> superficial data string can be so the comparison of memetic algorithms to >> recombination in genetic algorithms seems fanciful. Secondly the idea that >> the attributes of a concept might be clearly differentiated in an automated >> system that is able to learn and then used to clearly integrate different >> ideas seems unlikely. I do not think the concept is impossible, I think >> that it is complicated. It is a problem of complexity. You mentioned that >> you thought you can avoid complexity by using many small search problems. >> Although I cannot point to this or that study which can drive this point >> home, I do feel that there is ample evidence that domain restricted >> learning has not worked in AI just because we need to use concepts outside >> of the domain in order to understand those concepts which are strongly >> within the domain. (By the way, here is where an imagined efficiency of >> using weighted evaluations can really turn to nonsense. You can't eliminate >> the need to look outside the domain to determine meaning or relevance >> just by putting a numerical value on how much a meme belongs to a >> particular domain.) >> >> So to simplify this (so it does not sound like the product of a chat bot >> to you), a fully automated system cannot be expected to reliably determine >> the attributes of a concept (or meme) in order to use them to flawlessly >> integrate concepts. So assuming that your program would be able to achieve >> some viable integration one has to assume that the integration would be >> very rough. Your program might be able to make a crude haphazard matrix >> of relations between the concepts but how would it continue to refine this >> process without encountering some serious complexity? >> >> Here is where the hollow functional identity argument (as I called it) >> turns to delusion. 'If the program could do some crude thinking then it >> would be able to use the same processes to do more sophisticated >> thinking.' This is a philosophy that many AI programmers live by. Is >> it reality? We all know from personal experience and observation >> that there is a limit to intellectual ability and those limits have >> something to do with complexity. To believe that you can avoid complexity >> just by keeping the search space to a minimum doesn't sound insightful. >> Does that method work for you personally? Of course not. It is obvious >> that you have been trying to find methods for your program by broadening >> the search space of your studies. This is just the opposite of what you >> formally declared was a solution to complexity. It is an obvious >> contradiction. When you can't figure something out you sometimes expand >> the search area and you sometimes limit the search area. These method >> don't always work but they are important steps in learning and trying to >> find solutions. >> >> Jim Bromer >> >> >> >> >> On Wed, Dec 5, 2012 at 12:18 PM, Piaget Modeler < >> [email protected]> wrote: >> >>> >>> Jim, your prior e-mail reads like you are either a chatbot or are >>> attempting NLP (Neuro-Linguistic Programming) or DHE. >>> >>> Just ask, my answer may be yes or no. My own reason for assisting is >>> that I'd like you to understand my approach. >>> >>> Differentiation IS conditional branching. Observation is receiving >>> sensory stimuli. Coordination means making inferences. >>> Integration is combining different concepts via their attributes akin to >>> crossover or memetic recombination. >>> >>> Please define verification. It may be what I call correlation. >>> >>> Cheers! >>> >>> Imagine, NLP via e-mail. Whooda thunk it? >>> >>> >>> ------------------------------ >>> Date: Wed, 5 Dec 2012 07:54:16 -0500 >>> >>> Subject: Re: [agi] Internal Representation >>> From: [email protected] >>> To: [email protected] >>> >>> I agree with Piaget's remark. >>> >>> I am going to conduct an experiment. I want to see if I can get you to >>> solve a problem for me. So I am going to keep track of our conversation by >>> keeping notes on particular issues related to this experiment. It is >>> unlikely that you would be able to solve a particular problem that is of >>> interest to me, so I am going to be looking for an unexpected solution to >>> some related problem that I will pick up somewhat serendipitously from our >>> conversation. The best way to get you to cooperate with me on this is to >>> get you talk about the thing you are interested in. However, the solutions >>> to the problems of your projects probably will not be the solutions to the >>> problems of my projects, so I have to find a way to get you talk about >>> something that is common to both of our projects. >>> So I have gotten you to describe some ways that your program can apply >>> imagination to problem solving. Your seem to acknowledge that integration >>> is a part of the process, but you haven't acknowledged that complexity is a >>> problem. So now, in order to get you to continue discussing this I have to >>> back off from talking about complexity and emphasize the problems of >>> 'verifying' and integrating internal projections. I will review your >>> message in response to my question of how your program will use >>> imagination, and I will copy that response into my notes. Now that I have >>> reviewed some of your previous messages I see that you mentioned Piaget's >>> comments on coordination before. Coordination seems to be very similar to >>> conceptual integration. I also found that you had told me that Michalski >>> had a fast inferencing method so that must be important to you for some >>> reason. >>> >>> So, to repeat myself for clarity. I am going to run a >>> subjective experiment for a couple of weeks. The goal is to get you to >>> solve a problem for me and I want to be able to note how I personally >>> integrate subject related serendipity into my knowledge structures >>> concerning the subject. It is unlikely that you would be able to solve a >>> problem that I specified in advance so I am going to look for an unexpected >>> serendipitous solution to some problem that I haven't yet >>> completely identified. In order to get you to participate in this >>> experiment I need to encourage you to talk about your project using terms >>> that are relevant to both of us. Since I will be keeping notes I have >>> started by reviewing and collecting some of the comments you made in this >>> thread. I can then use this knowledge to get you to continue talking about >>> things that interest you. I noted that you have not acknowledged that >>> complexity is a problem so I will back off that particular problem and try >>> to shift to integration (coordination) issues that seem challenging for an >>> automated AGI program to use effectively. Now that I have explained this >>> 'experiment' to you I will stop talking about it and get back to the >>> subject. >>> >>> >>> On the list of mental coordination methods, internal simulation methods >>> and inferencing you did not specifically mention conditional branching so >>> there is a chance that you (or Piaget) left that off the list. I would say >>> that is a pretty important concept! On the other hand, running different >>> methods to use in a comparison with perceived events seems to imply a >>> conditional branching. >>> >>> >>> Anyway, the next question I have for you concerns 'verification' and >>> integration (coordination). Without strong verification, coordination is >>> essentially going to tie weak inferences together. If you accept that this >>> could be a problem then how would your program use the products of >>> coordination reliably? >>> >>> Jim Bromer >>> >>> >>> On Tue, Dec 4, 2012 at 11:45 PM, Piaget Modeler < >>> [email protected]> wrote: >>> >>> "The central idea is that knowledge proceeds neither solely from the >>> experience of objects nor from >>> an innate programming performed in the subject, but from successive >>> constructions, the result of >>> constant development of new structures.” ~ Jean Piaget**** >>> >>> >>> So I think we knit together these insights, piecemeal, until they recur >>> and strengthen, and become >>> more predictable and forceful in our minds. Then they integrate and >>> form a larger structure, and >>> eventually they become a subsystem, integrating with other subsystems, >>> until they finally integrate >>> with the totality. >>> >>> >>> Or at least that's how I interpreted it in "The Development of Thought" >>> by J.Piaget. >>> >>> >>> Cheers. >>> >>> >>> ~PM. >>> >>> >>> ------------------------------ >>> Date: Tue, 4 Dec 2012 23:12:06 -0500 >>> >>> Subject: Re: [agi] Internal Representation >>> From: [email protected] >>> To: [email protected] >>> >>> Well, I would look at Ryszard Michalski's work on dynamically interlaced >>> hierarchies if it was convenient for me to do so. Nothing about this is >>> mentioned on his home page and the first reference I looked at did not seem >>> like a breakthrough paper. >>> >>> I want to finish something that I was thinking about. >>> >>> We (or a machine) would be able to build strong knowledge if the >>> knowledge that was gained could be used to reliably predict, explain or >>> produce a specific outcome. But often, the outcomes are weak or unreliable >>> indicators of much of value. So instead we are left with a lot of weakly >>> related situation-action-reaction insights that are inexplicably >>> conditional and variant. >>> >>> This is a lot like serendipitous learning. If I try to learn something, >>> I probably won't be able to figure out what I wanted to figure out (unless >>> it is something that other people had already figured out and it was within >>> my field of knowledge). But I would probably learn something new >>> serendipitously. Now can we patch a lot of weak unexpected insights >>> together? Yes, but in order to build something reliable out of a lot of >>> weak structural pieces they have to be integrated pretty thoroughly. The >>> integration does not have to perfect but the matrix of these things have to >>> be strong enough to serve as a foundation for greater insights. >>> >>> Jim Bromer >>> >>> On Tue, Dec 4, 2012 at 9:31 PM, Piaget Modeler < >>> [email protected]> wrote: >>> >>> >>> I would agree that you also need mult-strategy reasoning in addition >>> to correlations. >>> >>> Look at Rysard Michalski's work on dynamically interlaced hierarchies. >>> He has a fast and efficient mechanism for inference. He inspired me. >>> >>> Cheers, >>> >>> ~PM. >>> >>> >>> ------------------------------ >>> Date: Tue, 4 Dec 2012 18:36:20 -0500 >>> >>> Subject: Re: [agi] Internal Representation >>> From: [email protected] >>> To: [email protected] >>> >>> I discovered something about logic that I never knew before. It is >>> something that I have thought about for 40 years, but I never stopped to >>> explore the application. Now, shouldn't this new insight give me greater >>> understanding? Well, yeah, but it doesn't work that way. I have a new >>> insight but I haven't got any use for it. So now I have to try to find >>> some practical use for it. Well even though I don't have any use for it, I >>> might pick up some street creds by telling other people about it right? >>> Well no, not really. It is really a turn-the-crank kind of thing and the >>> fact that I thought about it for so long without ever once examining its >>> application is kind of embarrassing. So now, before I can talk about it I >>> have to search for some way to use the idea effectively. If I found some >>> utility for it then I could pick up some credit for it, but until then it >>> is just going to make my work with logic more complicated. >>> >>> The insight was a turn-the-crank kind of insight so it represented the >>> application of a familiar idea onto another familiar idea in a way that was >>> very familiar to me. The only thing I did different was to actually see >>> how it worked in a few examples. When I did that I realized that the >>> effects were not exactly what I expected. However, logic is an artificial >>> field which is well formed so that other logic-based ideas, like something >>> from mathematics, can sometimes be easily integrated into it. In real >>> world examples of ideative projection, the analysis of turn-the-crank >>> imagination cannot easily be achieved just by using other (integrated or >>> related) methods of internal ideative projection. And as I just explained, >>> simple correlation methods are not an easy substitute for insightful >>> methods. >>> >>> Jim Bromer >>> >>> >>> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >>> <https://www.listbox.com/member/archive/rss/303/19999924-5cfde295> | >>> Modify <https://www.listbox.com/member/?&;> Your Subscription >>> <http://www.listbox.com> >>> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >>> <https://www.listbox.com/member/archive/rss/303/10561250-470149cf> | >>> Modify <https://www.listbox.com/member/?&;> Your Subscription >>> <http://www.listbox.com> >>> >>> >>> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >>> <https://www.listbox.com/member/archive/rss/303/19999924-5cfde295> | >>> Modify <https://www.listbox.com/member/?&;> Your Subscription >>> <http://www.listbox.com> >>> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >>> <https://www.listbox.com/member/archive/rss/303/10561250-470149cf> | >>> Modify <https://www.listbox.com/member/?&;> Your Subscription >>> <http://www.listbox.com> >>> >>> >>> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >>> <https://www.listbox.com/member/archive/rss/303/19999924-5cfde295> | >>> Modify <https://www.listbox.com/member/?&;> Your Subscription >>> <http://www.listbox.com> >>> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >>> <https://www.listbox.com/member/archive/rss/303/10561250-470149cf> | >>> Modify <https://www.listbox.com/member/?&;> Your Subscription >>> <http://www.listbox.com> >>> >> >> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >> <https://www.listbox.com/member/archive/rss/303/23050605-2da819ff> | >> Modify <https://www.listbox.com/member/?&;> Your Subscription >> <http://www.listbox.com> >> > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/10561250-470149cf> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
