Our assumptions about AGI define us. This is good. It means that there will be a variety of approaches, in a systematic and hopefully scientific manner, and perhaps some approaches will be viable where as others will prove ineffective. That's what inquiry is all about. If we all agreed, we'd probably get nowhere faster, because of our blind spots. ~PM Date: Mon, 30 Dec 2013 11:53:23 -0500 Subject: Re: [agi] Problem [Space] formulation From: [email protected] To: [email protected]
I really do not see how the assumption that an AGI program can detect features other than the primitive features of a sensor can be taken as a grand stepping off point for the higher level analysis and reasoning that you want to design into a program. John Rose talked about the correlative structures of the world, and this might be used as a good basis of further analysis of how an intermediate stepping off point would work. But the idea that something like autoencoders, PCA or perception and categorization are sufficient to explain how features may detected from the sensor environment should be something that, if the theory was true, would be feasible to prove right now - like my claim that an AI program could learn to use CF Grammars through trial and error learning is something that should be easy to demonstrate. If the significant features that were needed for an intelligent comprehension of events of a data environment were easy to detect then AGI would be easy. The whole problem has been that this initial stage of the detection of effective features has not been easy. Jim Bromer On Mon, Dec 30, 2013 at 3:13 AM, Piaget Modeler <[email protected]> wrote: I agree that goal directed behavior is at the top of the pyramid of functionality, with perception (observation) and categorization (coordination) at the base. I disagree because I do believe Babies plan. I also believe babies featurize their environment. I think they use the features to plan. One basic plan that babies employ to achieve their goals is called "Cry". Babies use that plan in numerous situations, and possibly when all other plans fail. My question was different. It was, given a grand number of features available at any given point, how do we choose the most "relevant" ones in order to formulate the current state. Or in the case of a planner, an initial state. A few approaches exist: (1) Pick a few features at random; (2) sort the features in some way and pick the first N features; (3) pick no features, and instead plan using an empty initial state and a goal state; (4) see if any correlation exists (temporal concurrence, temporal sequence, etc.) between features and the goal, if so, pick the initial state features based on this correlation. Doubtless there are other possible answers as well. Perhaps experimentation with all these approaches should be done to determine which works best. ~PM Date: Sun, 29 Dec 2013 22:54:58 -0600 Subject: Re: [agi] Problem [Space] formulation From: [email protected] To: [email protected] First comes awareness, then comes play, then comes planning. Babies don't plan anything. They just observe and correlate. They pick out salient features based on what allows them to predict the most about future observations, particularly those relating to reward or suffering. (Human faces and voices, among other things, are probably recognized a priori as salient through hardwired mechanisms.) Play allows the non-goal directed testing of observed correlations between salient features. Planning can only take place after you have started to recognize that salient feature X leads to salient feature Y, which is associated with reward or relief from/avoidance of suffering. Build the foundation first, in the form of a salient feature recognition engine -- an autoencoder* or some type of autocorrelation technique -- and then implement your planning engine on top of that. Goal directed behavior is at the top of a pyramid of functionality with perception and categorization at the base. *Not sure if you're familiar with autoencoders. They are a technique used for deep learning, among other things. The idea is to teach a pair of neural networks or other classification-learning algorithms to learn a compressed, (nearly) lossless encoding, which by necessity pulls out the most salient features of the input, and then to use the compressed representation as the input to the next layer, or in your case, the input to the planning engine. (The decoder is typically thrown away once the encoder/decoder pair has been trained.) It's similar in function to principal component analysis, which could also possibly serve your purposes here. On Sat, Dec 28, 2013 at 11:14 AM, Piaget Modeler <[email protected]> wrote: Thanks Jim, Much to think about... ~PM Date: Sat, 28 Dec 2013 08:33:22 -0500 Subject: Re: [agi] Problem [Space] formulation From: [email protected] To: [email protected] You could, for example, try to write a planner for solving the problem of selecting the relevant factors to form an initial state (and all the other possible states) for your lower solver to work with. To do this you would probably have to use some kind of abstractions. Since you want it to be based on solid scientific methods, you might try something like, material objects, properties and relations between objects. This you might find would not work so well since you want an AGI program to deal with imaginary things like fantasies and creative solutions to problems. So then you might start by working with something like ideas, concepts and conceptual relations. However, this does not solve the representation problem so then you have to include something about representations. However, you are back to the earlier problem, you cannot 'represent' every thing that you can think about so you might try to find a slightly better word-concept to use, like 'symbols' or 'references'. Even though you cannot 'represent' everything there is about the universe, for example, you can still refer to it. Since you will need a way to represent the symbolic references you might try to rely on grammatical primitives like 'nouns' and 'verbs'. This however implies that every concept-situation you might want your solver to represent could be represented with individual words. This does not quite make sense so you might then use actions and objects as part of your native abstractions for the solver to use in solving the problem of building a state system (or other kind of system) for a higher level (or more primitive) solver to solve. And you might try to think about using something like computational syntax instead of linguistic syntax, since, your solver is going to be a computational program and it will act as if it operating on computational strings anyway. These ideas however, still do not solve the problem because it is much too abstract and too full of gaps. And while the abstractions may seem like they are ideal for representing any possible situation, it turns out they are not because they are themselves possible states of thought. So, for another example, can you have a representation of something that cannot be represented? Well yes, since you can represent a reference to it. But that means that your solver has to be on guard against getting caught into illogical representations that refer to things that cannot be referred to in the way your abstract program is going to try to refer to them. While the comprehension and representation of the entire universe (for example) may not represent much of a problem to anyone other than a fool, it does show how problems of symbolic representations and references can creep into your solver when you start with higher abstractions. So now you need to add an incremental trial and error method to detect such possibilities. And since such things may be hidden by more complicated systems of references the problems cannot be easily detected. And incremental methods may not be strong enough to gain traction for your solver to create possible plan states (or other plan domains) so your solver solver will have to be able to latch onto something that will build traction into its incremental trial and error methods as it progresses in its goal to create a domain that is suitable for a planner. Oops. I am talking too much about my own ideas again. Sorry. However, it all seems so relevant to the situation that you just described, and so relevant to the question of the problem of creating an actual AGI program that I can excuse myself. On Fri, Dec 27, 2013 at 11:34 PM, Piaget Modeler <[email protected]> wrote: So I'm writing my solvers, and I hit the first roadblock... Given (1) a sea of sensory stimuli, (2) a prioritized set of goals, and (3) a possibly empty set of plans, how does one select the relevant stimuli to form an initial state for a problem? Another way to ask the question is how do humans select relevant features from their current environment to be able to formulate or retrieve plans that address their goals? And how many of these environmental features are enough to describe an initial state? So there is PDDL. (Planning Domain Definition Language). But to use PDDL, one has to first solve the problem of describing the relevant features of the environment. How do we come up with these relevant features, to be able to formulate an initial state? Thoughts? ~PM (I forgot what Newell & Simon said about the PSCM & Unified Theories of Cognition. 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